{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A definitive guide to effect size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pingouin as pg\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# plotting\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effect size families"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. The \"correlation\" family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the parameters\n",
    "nobs = 10**5\n",
    "mu = [1, 1.5]\n",
    "cov_matrix = np.array([[1, 0.6],\n",
    "                      [0.6, 1]])\n",
    "\n",
    "# generate the correlated random variables \n",
    "np.random.seed(42)\n",
    "x = np.random.multivariate_normal(mu, cov_matrix, size=nobs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pearson's r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.60236704],\n",
       "       [0.60236704, 1.        ]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(x, rowvar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6023670412294826, 0.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(x[:,0], x[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>r</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>r2</th>\n",
       "      <th>adj_r2</th>\n",
       "      <th>p-val</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pearson</th>\n",
       "      <td>100000</td>\n",
       "      <td>0.602367</td>\n",
       "      <td>[0.6, 0.61]</td>\n",
       "      <td>0.362846</td>\n",
       "      <td>0.362833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              n         r        CI95%        r2    adj_r2  p-val BF10  power\n",
       "pearson  100000  0.602367  [0.6, 0.61]  0.362846  0.362833    0.0  inf    1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.corr(x[:, 0], x[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Coefficient of determination (R²)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>names</th>\n",
       "      <th>coef</th>\n",
       "      <th>se</th>\n",
       "      <th>T</th>\n",
       "      <th>pval</th>\n",
       "      <th>r2</th>\n",
       "      <th>adj_r2</th>\n",
       "      <th>CI[2.5%]</th>\n",
       "      <th>CI[97.5%]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>0.893873</td>\n",
       "      <td>0.003561</td>\n",
       "      <td>251.004199</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.362846</td>\n",
       "      <td>0.36284</td>\n",
       "      <td>0.886894</td>\n",
       "      <td>0.900853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x1</td>\n",
       "      <td>0.601396</td>\n",
       "      <td>0.002520</td>\n",
       "      <td>238.635290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.362846</td>\n",
       "      <td>0.36284</td>\n",
       "      <td>0.596457</td>\n",
       "      <td>0.606336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       names      coef        se           T  pval        r2   adj_r2  \\\n",
       "0  Intercept  0.893873  0.003561  251.004199   0.0  0.362846  0.36284   \n",
       "1         x1  0.601396  0.002520  238.635290   0.0  0.362846  0.36284   \n",
       "\n",
       "   CI[2.5%]  CI[97.5%]  \n",
       "0  0.886894   0.900853  \n",
       "1  0.596457   0.606336  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.linear_regression(x[:, 0], x[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th>  <td>   0.363</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.363</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>5.695e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 16 Jun 2020</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:26:16</td>     <th>  Log-Likelihood:    </th> <td>-1.1938e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>100000</td>      <th>  AIC:               </th>  <td>2.388e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 99998</td>      <th>  BIC:               </th>  <td>2.388e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.8939</td> <td>    0.004</td> <td>  251.004</td> <td> 0.000</td> <td>    0.887</td> <td>    0.901</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.6014</td> <td>    0.003</td> <td>  238.635</td> <td> 0.000</td> <td>    0.596</td> <td>    0.606</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.991</td> <th>  Durbin-Watson:     </th> <td>   1.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.370</td> <th>  Jarque-Bera (JB):  </th> <td>   1.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.003</td> <th>  Prob(JB):          </th> <td>   0.371</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.979</td> <th>  Cond. No.          </th> <td>    2.61</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.363\n",
       "Model:                            OLS   Adj. R-squared:                  0.363\n",
       "Method:                 Least Squares   F-statistic:                 5.695e+04\n",
       "Date:                Tue, 16 Jun 2020   Prob (F-statistic):               0.00\n",
       "Time:                        00:26:16   Log-Likelihood:            -1.1938e+05\n",
       "No. Observations:              100000   AIC:                         2.388e+05\n",
       "Df Residuals:                   99998   BIC:                         2.388e+05\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.8939      0.004    251.004      0.000       0.887       0.901\n",
       "x1             0.6014      0.003    238.635      0.000       0.596       0.606\n",
       "==============================================================================\n",
       "Omnibus:                        1.991   Durbin-Watson:                   1.999\n",
       "Prob(Omnibus):                  0.370   Jarque-Bera (JB):                1.982\n",
       "Skew:                          -0.003   Prob(JB):                        0.371\n",
       "Kurtosis:                       2.979   Cond. No.                         2.61\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(x[:, 0])\n",
    "model = sm.OLS(x[:, 1], X).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Eta-squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.057968511053166284"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.compute_effsize(x[:, 0], x[:, 1], eftype='eta-square')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. The \"difference\" family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x126d5cf98>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 329,
       "width": 492
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define the parameters\n",
    "nobs = 10**5\n",
    "\n",
    "# generate the random variables \n",
    "np.random.seed(42)\n",
    "x = np.random.normal(1, 1.5, nobs)\n",
    "y = np.random.normal(2, 2, nobs)\n",
    "\n",
    "# plot the variables\n",
    "sns.distplot(x, color=\"skyblue\", label=\"x\")\n",
    "sns.distplot(y, color=\"red\", label=\"y\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cohen's d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5661743543595718"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.compute_effsize(x, y, eftype='cohen')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Glass' Δ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6664041092152272"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.compute_effsize(x, y, eftype='glass')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hedges' g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5661722311818571"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.compute_effsize(x, y, eftype='hedges')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. The \"categorical\" family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the parameters\n",
    "nobs = 10**4\n",
    "choices = [0, 1]\n",
    "\n",
    "# generate the random variables \n",
    "np.random.seed(42)\n",
    "x = np.random.choice(choices, size=nobs, p=[0.5, 0.5])\n",
    "y = np.random.choice(choices, size=nobs, p=[0.6, 0.4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### φ (phi coefficient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phi coefficient: 0.000944\n",
      "Matthews Correlation: 0.000944\n",
      "Pearson's r: 0.000944\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import matthews_corrcoef\n",
    "\n",
    "def get_phi_coeff(x, y):\n",
    "    n = len(x)\n",
    "    cont_table = pd.crosstab(x, y)\n",
    "    chi_2 = stats.chi2_contingency(cont_table, correction=False)[0]\n",
    "    phi = np.sqrt(chi_2 / n)\n",
    "    return phi\n",
    "\n",
    "phi = get_phi_coeff(x, y)\n",
    "mcc = matthews_corrcoef(x, y)\n",
    "r = stats.pearsonr(x, y)[0]\n",
    "\n",
    "print(f'Phi coefficient: {phi:.6f}')\n",
    "print(f\"Matthews Correlation: {mcc:.6f}\")\n",
    "print(f\"Pearson's r: {r:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cramér's V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cramer's V: 0.000944\n"
     ]
    }
   ],
   "source": [
    "def get_cramer_v(x, y):\n",
    "    n = len(x)\n",
    "    cont_table = pd.crosstab(x, y)\n",
    "    chi_2 = stats.chi2_contingency(cont_table, correction=False)[0]\n",
    "    v = np.sqrt(chi_2 / (n * (np.min(cont_table.shape) - 1)))\n",
    "    return v\n",
    "\n",
    "cramer_v = get_cramers_v(x, y)\n",
    "\n",
    "print(f\"Cramer's V: {cramer_v:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cohen's w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cohen's w: 0.173820\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.gof import chisquare_effectsize\n",
    "\n",
    "cohen_w = chisquare_effectsize([np.sum(x), np.sum(x == 0)], \n",
    "                               [np.sum(y), np.sum(y == 0)])\n",
    "print(f\"Cohen's w: {cohen_w:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cohen's h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cohen's h: 0.174943\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.proportion import proportion_effectsize\n",
    "\n",
    "cohen_h = proportion_effectsize(np.mean(x), np.mean(y))\n",
    "print(f\"Cohen's h: {cohen_h:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Odds Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Odds Ratio: 1.374506\n"
     ]
    }
   ],
   "source": [
    "odds_ratio = pg.compute_effsize(x, y, eftype='odds-ratio')\n",
    "print(f\"Odds Ratio: {odds_ratio:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Common Language Effect Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLES: 0.779395\n"
     ]
    },
    {
     "data": {
      "image/png": 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66881cOBgde3aTZJ03333aMWKZQl6zZQkjRgxSg8/3HBwjnXGGeN0xhnjVF5eriVLFmnBgg81d+7b+v3vf6W+ffvaC4k5qclj388++6wk6Sc/+Yk9/9wwDP3sZz+TYRh68cUXE+7/3HPPHTKCnJ2drRtuuEHV1dVx07LXrVunUCikQYMGNbVtAAAAAEgJhYUDJEmrVq045LWysv164IE/a/bsWc12vrlz35Yk/fa3/6fTTx9rh2ZJ2rChOOG+2dnZ6tq1m4qL1ysQqDnk9bfeelP/+MfftHXrFoVCIT311D/0/PO1mTInJ0dnnDFOv/zlb3XVVVMVjUa1dOnXzfa+mqLJwXnRokXq2LGjBg4cGPd8165d1adPH33++ecN7ltSUqLt27dr9OjRcrlcca+NGTNGkuL2LyoqkiSCMwC0VdGo0x0AANDqvvWtccrKytKzzz6t4uL44PrXv07Xiy/+W6Wlm5rtfF5v7VTuvXv3xD3/1ltv6ssvl0iqXeeqIZMmna+ysv165JGHFI353V1cvF733/8nPf/8c2rXrp08Ho/mzp2txx//mzZvLo07xtatWyTpkOu0ndKkqdrBYFDbtm3TiBEj6n29oKBAxcXF2rNnjzp16nTI6yUlJZKkXr16HfJaXl6efD6fNmzYYD93IDhv2LBBl112mYqKiuT3+zV27Fjdcsst6tq1a1PeDgAg2ViWzG1b5f7qS7m//lLmzh0KDxuhwH99R1ZWttPdAQDQKnJycvTzn/9Gv//9r3TxxRfrrLPOUk5OB33xxWKtXLlCQ4Yco8sv/36znW/ChEl69905+uUvb9NZZ01QVlaWVqxYri+/XKKOHTtp79492r9/X4P7f+97V+mzzz755rrlLzRq1GiVl5dr3rx3VVNTrd/85n+V9c3v8Rtu+LF+8YvbNHXq9zRu3FnKyWmnoqKVWrz4c40ceZxOOGFMs72vpmhScN63r/aHlZOTU+/rB54vLy+vNzgf2L9du3b17p+dna3y8rq5+weC88MPP6yzzz5bI0eO1FdffaVXXnlFCxYs0AsvvKBu3brVe6zGyMur/32gafi5Ih3xuW5hRUXSe7OlxYul7dvjXvJ8/aU8JRukqVOlmNlOOfw7aRZ8tpGO+FynmZyGF71KBs3x+yjnm/eYleWzP7+TJ1+sgQP76G9/+5vmz5+v6upqFRQU6MYbb9TUqVOVnV33B2WXy5RhGId89r3e2viXm5utdu3qXvP7a++E1LFjlvLycnThhRPl8Vh67LHHNHfu2/L7/erZs6fuvPNOjRo1ShdffLG++GKhrrjiOwoEsuxj150vR//+97N6/PHHNWvWLL366kvKycnR6NHH6frrr9eJJ55on/vii89Tly4d9dhjj+mTTz5SWVmZ8vPz9eMf/1jXXnutfX230wyroeXQGmHLli0aN26cxo8fr0ceeeSQ12+//Xa9/vrrmjFjxiFTuSXptdde089//nP94he/0A9+8INDXh87dqyqq6v12WefSZJ+/OMfa+3atXrooYc0YMAAe7tHHnlEf/nLX3T22WfroYceOtq3AwBIBk8/XRuKE0wBkyQZhnTuubX/mKZ03XWt0x8AAGhzmjTifGCJ81AoVO/rwWDtfcAa+iuBz+eL266+/TMzM+364Ycfrne766+/Xi+99JLmzZunyspKZWVlNe4NHKQlV6Zriw78xYmfK9IJn+uW5Zn/gdpPnSqjntAczc2TolGZe2rv/yjLkt58U+EVK1VzxRRV8++kSfhsIx3xuUY64nPdNEc7A6VJwTk7O1umaaqioqLe1w9Ms25oKnf79u0lqcH9Kyoq7FtRJWKapgYPHqzS0lJt27ZNhYWFjWkfAJBEXGtWq901348LzdHcPIVGjFJ4xEhFu+dLgYD8r7wgz5LF9jbu9euUef+9Ck46T5H+A+o7NAAAQJM0KTh7vV7l5+ertLS03tdLS0vVsWNHdejQod7X+/TpY293sB07digQCKhv376SpOrqansxsMGDBx+yfU1N7VLnB0axAQCpw9i9W+2vuFRmzEIjoZGjVHPFlNpp2Af4/aq5/PsKDxgk/ysvyQjVzlgyKyuV/fOfaf9Lb9RO4QYAAGhGTb4d1ejRo7Vz585DlkXfvn27Nm7cqJEjRza4b35+vvLz87V48eK4ZcolaeHChZKkUaNGSZJ27dqlyZMn63/+538OOU51dbVWrFihTp06qaCgoKlvCQDQmmpq1P6qy+XauMF+KjT6BNVMviI+NB9gGAqfMEZVP71NkZj7SnrnfyDvN/edBAAAaE5NDs4XXXSRJOn++++3w69lWZo2bZosy9LkyZMT7n/BBRdo27ZteuaZZ+znKioq9Oijj8rv9+vCCy+UJPXs2VNDhw7V6tWr9cYbb9jbWpalP//5z9qzZ48uv/xyGYw0AEDqsCzl3PJjeRZ+aj8V6dVb+5/6t+TxJtw12qWrar53layY/+9n//Qm+Z94XP6nnzjsPwAAAI3VpKnaknTKKado0qRJmjVrliZPnqwxY8boiy++0KJFizRhwgSNHTvW3vbBBx+UJN100032c9dee63efvtt3X333fr888/Vs2dPzZkzR5s2bdJvfvObuNtY3XXXXfr+97+v22+/XXPmzFFBQYEWLVqkZcuW6YQTTtANN9zQ1LcDAGhFmff/Sf5XXrTraE477X/2RVldujRq/2j3fIXGnCzvpx9Lklw7d8jz6ccKnXp6i/QLAADapibdjuqAUCikv//973r11Ve1fft25efn64ILLtC1114rr7duxGDQoEGS6u7HfMCuXbs0bdo0zZs3T9XV1erXr5+mTp2qc88995BzrVu3TtOnT9enn36qyspKFRQU1Huuo8HKdM2LFf+QjvhcNx9X0Sp1HHuyjEhEkmS5XNr/75cVGjtekho9KmyUlynrj/8nIxCQJEUzs1T5i19LGZkJ96uZcnUTuk8/fLaRjvhcIx3xuW6ao11Vu1mCc7rgw9e8+I8a6YjPdTOxLLX/zkXyfjjPfqr83vtV84Opdn0k06m9786R762Zdh0cO16B8y5MuA/BOR6fbaQjPtdIR3yum+Zog3OTr3EGAOBIed+aGReag6efoZqrrjnq4wW/NVbRmDs4eOZ/IGP3rib1CAAAcECTr3EGAKAx7BHkUFBZ995jP2+ZpkInniT/v548+oN7vApMOl8Zz/1LkmREIvLNnMGoMgAAaBaMOAMAWpX3g3ky9+6x69AppynaPb/Jxw2PPE6Rnr3s2vP1lzI3FCfYAwAAoHEIzgCAVmPs2yvvu3PtOpqZpcA5E5vn4KapwAUXxT3lmzmjeY4NAADaNIIzAKDV+N58Q0YoZNfBiedKmYlXvz4Skb6FCg0bYdfu4nUyt21ttuMDAIC2ieAMAGgVrnVr5flyiV1H8gsUGnNys58nOO7MuNqz8NNmPwcAAGhbCM4AgJYXDsv32stxTwUu/i/JbP5fQ9GevRTJL7Br96LPpXC42c8DAADaDoIzAKDF+V56Xq6tW+w6NGq0In0LW+ZkhqHQiSfZpVlVKfeypS1zLgAA0CYQnAEALSscVta0e+3S8ngUOPeCFj1l6LjjZbnr7rjoWfhJi54PAACkN4IzAKBF+V56Xq6Y20KFTj1dVocOLXvSzEyFh8csEra6SMbu3S17TgAAkLYIzgCAlnPIaLNXwTPGt8qpQyfGLzzm+ZxFwgAAwNEhOAMAWozv5RfiR5tPOU1WTk6rnDtS2F/R3Fy79nz+mRSJtMq5AQBAenEffhMAAA7lf/qJxBtEIsr60x/s0vJ4FBzbOqPNkr5ZJOxk+WbNkCSZ+/fLVbRKkWOGtl4PAAAgLTDiDABoEe4vFsvctcuuQ6ec3mqjzfY5jz9RVswtr1gkDAAAHA2CMwCg+UUi8r0z2y5rR5vHtXobVrt2CseMMLtXLJdRVtbqfQAAgNRGcAYANDv3l0sOGm0+TVZOO0d6iV0kzIhG5Vn0mSN9AACA1EVwBgA0r0hEvrkHjza34rXNB7czaLCi7dvbteezTyXLcqwfAACQegjOAIBmVTvavNOuQyef6thosyTJ5VLohJPs0ty9S67i9c71AwAAUg7BGQDQfKJRed+da5e1o81nOthQrdAJY+Jq95dfONQJAABIRQRnAECzca9YJteO7XYdOukUWe0cHG3+htW5syK9etu1e+mX3NMZAAA0GsEZANA8LEved9+pK10uBc9w7trmg4VGjLIfm+Xl8nyywMFuAABAKiE4AwCahWvdWrk2bbTr0OjjZXXo4GBH8cIjRsbVvtdfdagTAACQagjOAIBm4X0vZrTZMBRKgmubY1kdOirSp69d+2a+LoXDDnYEAABSBcEZANBkZukmuVevsuvwscMU7dLVwY7qFzdde9cueRbMd7AbAACQKgjOAIAmix1tlqTguLMc6iSx8PARsgzDrn2vv+JgNwAAIFUQnAEATWLs3CH30q/sOjxgoKIxK1gnE6t9B0X69LNr38w3pFDIwY4AAEAqIDgDAJrE+/57MizLrpN1tPmA8MiY6dp798oz/33nmgEAACmB4AwAOGrG/v3yLFpo15EePRUZMNDBjg4vPOzg6dqsrg0AABIjOAMAjpr3w/dlRCJ2HRx/lhQTSpOR1a6dIv3627Vv1ptSMOhgRwAAINkRnAEAR6eqSp5PF9hlNK+LwscOd7Chxou9p7O5f5+8H7znYDcAACDZEZwBAEfF+/FHMgIBuw6OHS+ZqfFrJTx8pKyYXn2vsbo2AABoWGp8wwEAJJeqqrhFtaLt2ys0+gTn+jlCVna2QqedYdfet2dJNTUOdgQAAJIZwRkAcMT8//6XzMpKuw5+a5zkdjvY0ZELXHSJ/dgsL5N33rsOdgMAAJIZwRkAcGRCIWX+9UG7tDIyFRpzsoMNHZ3ApPNkxYR93xusrg0AAOpHcAYAHBHfqy/JtanEroOnni75/Q52dHSsTp0VOj1muvbc2ayuDQAA6kVwBgA0XjSqzIf+YpeWx6PQ6d9ysKGmCUw6335slu2X56MPHewGAAAkK4IzAKDRvHNny71qpV2HxpwsKyvbwY6aJvDtc2XF3HfaN+tNB7sBAADJiuAMAGgcy1LmA3+uK01TwTPGOdhQ01lduyp84kl27XvrTSkScbAjAACQjAjOAIBG8Xz6sTyLFtp1+LjjZXXs5GBHzSNuuvbOHXJ/vjDB1gAAoC0iOAMAGiVj+rS4Ojj2TIc6aV6BSefF1b5ZMxzqBAAAJCuCMwDgsFzLlsr37ly7Dnz7XEW7dXOwo+YT7d1HoWOH27Vv1gzJshzsCAAAJBuCMwDgsLKm3RtXV938U4c6aRnBc+uma7tKNsq1bKmD3QAAgGRDcAYAJORasVy+N1+36+Bp31L4+BMd7Kj5Bc69IK72zXzDoU4AAEAyIjgDABLKPHi0+bY7HOqk5UQGDVa4X6Fdc50zAACIRXAGADTItWqlfDNes+vgKacpdMppDnbUQgxDwZhRZ/eqlXKtW+NgQwAAIJkQnAEADcq8/14ZMQtlpeNo8wGBmOucJck7802HOgEAAMmG4AwAqJdrdZF8r71i16ExJyt06ukOdtSywiOPU6R7vl37ZnGdMwAAqEVwBgDUK3Na/Ghz5W13SIbhYEctzDQVjLmns2fJYplbNjvYEAAASBYEZwDAIVxr18j32st2HTphjELfGutcQ63k4NW1vW8xXRsAABCcAQD1yJx2r4xo1K7TfrT5G6GTTlG0Uye79s0iOAMAAMntdAMAgObnf/qJI9q+ZsrV9mPX+rXyvfKiXYdGn6DQ2PHN1ltSc7sVmDBJGf9+RpLk+fgjGbt3y+rc2eHGAACAkxhxBgDEybz/vrjR5qrbft4mRpsPCMasrm1EIvLOecvBbgAAQDIgOAMAbGbxevleet6uQ6OOU3D82Q521PqC3xqnaFa2XftmzXCwGwAAkAwIzgAAW+Zf7pMRidh1VRu5tjmO36/gWefYpff992RUlDvXDwAAcBzBGQAgSTI3FMv/wr/tOjRylIJnTXCwI+fETdcOBOR57x0HuwEAAE4jOAMAJEmZ06fFjzbf2gZHm78RPPNsWV6vXftmvuFgNwAAwGkEZwCAzJKN8v/nWbsODRuh4DnfdrAjZzMPPx8AACAASURBVFk57RQ8Y5xde+fOkQIBBzsCAABOIjgDAJT5wDQZ4bBdV93atlbSrk/w3Avsx2ZFubzz33euGQAA4CiCMwC0ccbePfL/5xm7Dg8dpuDEcx3sKDkEzpkoy6z7NemdyeraAAC0VQRnAGjjvO+9IyMUsutKRpslSVZurkInn2rXvrdnSjGj8gAAoO0gOANAG2bs2yvPwk/tOjxkqIKTznOwo+QS+7Mwd++O+1kBAIC2g+AMAG2Y971341bSrrz1dsnkV8MBgUnnx9VeVtcGAKBNcjvdAADAGUZZmTyffWzX4cFDFDzvQgc7al3+p59o1HaRnr3k2lQiSfLNelOV//f/mMoOAEAbw7ACALRRnk8XxN+3+af/w2hzPcLHDrcfuzaXyv3VFw52AwAAnMA3JABoi8JheT5ZYJeRnr0UOP8iBxtKXqFhw+NqH6trAwDQ5hCcAaANcn/1hczycruuvvpayc3VO/WxunRVpGs3u/bOIjgDANDWEJwBoK2xLHnnf1BXeryq+d4UBxtKfrHTtd1rVsu1usjBbgAAQGsjOANAG2OWbJSrdJNdh0YfL6tDRwc7Sn7hg6Zre99606FOAACAEwjOANDGxI42S1LotDMc6iR1RAt6KNKjp137CM4AALQpBGcAaEOM/fvk/vpLuw4PGKhot24J9oAkyTAUmHiuXXqWLJa5dYuDDQEAgNZEcAaANsTzyQIZ0ahdBxltbrTgxPPiau/bsxzqBAAAtDaCMwC0FaGQPJ98bJfRzrmKDDnGwYZSS+ikUxTt0MGuma4NAEDbQXAGgDbC/eUSmZUVdh089XTJ5NdAo7ndCp4z0S49H30oY/8+BxsCAACthW9MANAWHHwLKp9PoRPGONhQagrETNc2wmF5353rYDcAAKC1EJwBoA1wFa+Xa8tmuw4df6KUkeFgR6kpOHa8LL/frr1vzXSwGwAA0FoIzgDQBrgXLYyrg6ee7lAnKS4rS8Gx4+3S+84cKRBwsCEAANAa3E43AABoYZGIPMu+tstwv/6yunR1sKHU5H/6CUlStF17+zmzskJZv/t1vYus1Uy5utV6AwAALYsRZwBIc661q2VUVdl1eMRIB7tJfZFjjpVlGHbtXr7UwW4AAEBrYMQZANKc+6sv7ceWYSg8bMQh2xwYTcXhWdnZivTtJ/f6dZJqg3Pgku+wQjkAAGmM3/IAkM4OmqYd6Vcoq107BxtKD+Fjh9mPzfJymSUbHewGAAC0NIIzAKSxQ6ZpD2eadnMIDx0WV7uXMV0bAIB0RnAGgDTWmGnaOHJW51xFuufbtWfZ15JlOdgRAABoSQRnAEhXB0/T7ss07eYUO+ps7topc8d2B7sBAAAtieAMAGmK1bRbVux1zhLTtQEASGcEZwBIU0zTblnRgh6Kduhg1wRnAADSF8EZANIR07RbnmEofOxwu3Rt2ihj/z4HGwIAAC2F4AwAaci1dg3TtFvBIatrL1/mUCcAAKAlEZwBIA25v/rCfsw07ZYT6VcoKyPTrpmuDQBAeiI4A0C6CYWYpt1aXC6FjxlaV65dLVVXJdgBAACkIoIzAKQZz0cfMk27FcWurm1Eo3KvWulgNwAAoCUQnAEgzfjeeNV+zDTtlhceOFiW22PXTNcGACD9NEtwDofDevLJJzVp0iQNHz5cZ555ph5++GGFQqFG7b9v3z7dddddGj9+vEaMGKFLLrlEs2bNatS+N998swYNGqTS0tKmvAUASA+WJd+ct+2SadqtwOdTZOAgu3SvWiGFww42BAAAmluzBOe77rpL99xzjzp06KApU6aoa9eumj59um699dbD7ltVVaVrrrlGzz33nEaMGKErr7xSZWVl+ulPf6pnnnkm4b5z5szR7Nmzm+MtAEBacK0ukrlzh13HXn+LlhOKna4dCMi1ZrWD3QAAgObmbuoBlixZoueff14TJkzQAw88IMMwZFmW7rjjDr322muaN2+exo0b1+D+Tz/9tJYvX64777xTV155pSTpxhtv1GWXXab77rtPEydOVOfOnQ/Zb//+/fr973/f1PYBIK14Pvogro4MGOhQJ21L5JhjZRmGDMuSJLmXM10bAIB00uQR52effVaS9JOf/ESGYUiSDMPQz372MxmGoRdffDHh/s8995xyc3N12WWX2c9lZ2frhhtuUHV1tWbMmFHvfn/4wx8UCoU0ciSL3gDAAd6P5tuPrYxMRbvnO9hN22FlZyvSp59du5cvlaJRBzsCAADNqcnBedGiRerYsaMGDowf1ejatav69Omjzz//vMF9S0pKtH37do0ePVoulyvutTFjxkhSvft/+OGHeu2113THHXfUOxoNAG1SNCrPx3XBOVxYKJmsAdlaYlfXNsvL5V7c8O8/AACQWpr0jSoYDGrbtm3q1atXva8XFBSorKxMe/bsqff1kpISSap3/7y8PPl8Pm3YsCHu+YqKCt1555065ZRTdMkllzSlfQBIK67ly2Tu3WvXkf5M025N4aHD4mrfWzMd6gQAADS3Jl3jvG/fPklSTk5Ova8feL68vFydOnVqcP92Daz4mp2drfLy8rjn/vSnP9mrcDe3vLz63weahp8r0lFSfq6/WhhX+ocPlT/H71AzbVBOD6mgQNq8WZKUOXumMqdPk765jClVJOVnG2giPtdIR3yuW1eTRpzD39xuw+v11vv6gecDgcBR7x+778KFC/X888/r5ptvVs+ePY+6bwBIS++9V/c4J0fK5/rmVhe77saaNdKqVc71AgAAmk2TRpz9/tqRjIbu1xwMBiVJGRkZ9b7u8/nitqtv/8zMTElSTU2Nfv3rX2vo0KG66qqrmtJ2g3buLD/8Rmi0A38F4+eKdJK0n+twWJ0/+ND+a2ioX3/VVNT/R0u0HHPAEGWpbop2xbPPq/q/D39rxmSQtJ9toAn4XCMd8blumqMdqW9ScM7OzpZpmqqoqKj39QPTrBuayt2+fXtJanD/iooKe/GvBx54QJs3b9bLL798yEJiAJDu/E8/kfB1s2SjzPIyu44U9m/pllCPaEEPRTt0kPnNpUi+t95MmeAMAAAa1qTg7PV6lZ+fr9LS0npfLy0tVceOHdWhQ4d6X+/Tp4+93cF27NihQCCgvn37SpJmz56tcDisCy+8sN5jnXnmmZKkoqKiI30bAJDy3GvXxNVh7t/sDMNQ+Njh8n70oSTJs2SxzK1buC0YAAAprknBWZJGjx6t119/XcXFxXbIlaTt27dr48aNGjt2bIP75ufnKz8/X4sXL1Y0GpUZc9uUhQtrF7kZNWqUJGnKlCmHLBQmSTNnzlRxcbGmTJnS4CJjAJDuXOvqgnO0XXtZuXkOdtO2hYcOs4OzJHnfnqWaq3/oYEcAAKCpmhycL7roIr3++uu6//779Ze//EWmacqyLE2bNk2WZWny5MkJ97/gggv06KOP6plnntGUKVMk1U7RfvTRR+X3++0R5h/84Af17r9y5UoVFxfrqquuUo8ePZr6dgAg9YTDcq1fb5eR/v1TbiXndBLpVygrI1NGdZWk2unarRmcV1VEGr2tZVmKSopYkj9cpcL2Xm2viSpkWfKZhnI9htwmnyUAAJocnE855RRNmjRJs2bN0uTJkzVmzBh98cUXWrRokSZMmBA34vzggw9Kkm666Sb7uWuvvVZvv/227r77bn3++efq2bOn5syZo02bNuk3v/lNvbexAgDUcW0qkRGqW2QxzP2bneVyKXzMUHkWfy5J8nz0oYz9+2S1r/+ypdYWilraE7K0K2Rpf9iSdeCFsirN31YVt60hqZPHUJ7XUBevqTyvofZuQwZ/mAEAtDFNDs6SdO+996p///569dVX9dRTTyk/P18333yzrr322rhfrg899JCk+OCcnZ2tZ599VtOmTdO8efM0f/589evXT9OmTdO5557bHO0BQFqLnaYtSZHCAQ51ggPCxw6zg7MRDsv77lwFLvnOER3jSEaOD9tP1NLu+sLyYViSdodq911VGZUk+UxpYKZLx+a4lOkiQAMA2gbDsqzG/v5Meyzp3rxYKh/pyKnPdaJVtTMeeUjub8JztGMnVf7qt63VFhoSCCj7f++UUVMjSaq58BKVP/bkER2iOYJzTcTShpqo9oQaH5YbyyVpYJap4TluldZEj+oYg7O5SwZaFt9FkI74XDeNI7ejAgA4LBSSa2OxXUb6M9qcFHw+Bc8YJ9/styRJ3nfmSIGA5PO1yumjlqXNAUulNVE1FGldqp2Gneky5DKk7AyP+rf3qrq8Rh5TKgtb2hm0tCMY1e6gdchxIpJWVka1qjKoPK+hHj5TGYxAAwDSFMEZAFKYa+MGGeGwXYcJzkkjOPE8OziblRXyLPhQofFnt/h594ctrauKqLqexGyqNizneg11dBsyYy6nysnyaGAHn3Z+c718nlcqzKx9LWJZ2h20tD0Y1arKqMrCdePXlqQdQUs7ghF18Rrqm2HKzTXQAIA0Q3AGgBTmWsv1zckqcM5EZZumjOg31wbPmtmiwTkUtbShOqodoUMnZWe7pB4+Ux08hlwJQu2Xu2pUnmCKuMcwdGyWqV2h2tHsqoPC+Y6gpfJwRIOzuP4ZAJBezMNvAgBIVnH3b87Nk9UhOVZuhmTl5ip04kl27X17phQ9umuBD2d3KKol5ZFDQrPLkAozTA3Pdqmz10wYmhvLMAzleU2NzHFpcJapgy9Tro5KX5VHtDPYMu8VAAAnEJwBIFUFAnKVbLRLpmknn+DE8+zHrh3b5V6yqNnPsTVQO306fNBAc57H0HE5LnXzmS1y+yjDMNTZUxvKh2SZ8sScIippdVVU66oiirIGKQAgDRCcASBFuUo2yojUTatlmnbyCXx7Ulzte2tmsx3bsixtqI5o/UEXM/tNaWiWqYFZLnnNlp8ubRiGOnlqR6DbHTT6vC1oaWlFRIEo4RkAkNoIzgCQolybNsbVkb79HOoEDYn27afwkKF27X3rzeY5rmVpTVVUmwPxgbSL19CoHJc6eFr/17vXNHRstksFvviwXhGRviyPqPzgIXEAAFIIwRkAUpS5qcR+HM1pJ6t9ewe7QUMCE8+1H7vXrpFrzeomHS9sWVpRGdXOg65n7uk31T/DjFspu7UZhqE+GbXXPscOPoctaUVlRFURwjMAIDURnAEgRblignOkVy+JWwAlpeCk8+Lqpow6B6KWllVEtP+g0dv+GaZ6+VvmWuaj0dljakSOS1kx3zLClrSCadsAgBRFcAaAFGSUlcnct8+uoz17OdgNEgkPG6FIQQ+79h1lcA5+E5orY+4WZUoakmWqqy/5fp1nuAwNy3HFrbod+CY8hwnPAIAUk3y/aQEAhxU7TVuSIgTn5GUYcdO1PYsXydy29YgOEbEsrayMqCZmHTCPIR2b7VInB65nbiyXYeiYLJf8MS1WRaWVlay2DQBILcn72xYA0KBDFgYjOCe12NtSSZL37VmN3teyLK2ujKoiZqTZZ0rDsl3KcSfH1OxEPKahoVmuuNtVlUVqb1dFeAYApAqCMwCkoNjrm6Odc6XMLAe7weGETjpF0fYd7Nr7zuxG71tcHdWemGua3YY0NMulDFfyh+YD/K7akefYBcN2hyx9ui8si/AMAEgBBGcASDWWdejCYEhuHo+C48+0S++H70tVVYfdbUsgqq3BumBpSBqSYqH5gGy3ocFZpmI7X1kZ1dflkQb3AQAgWRCcASDFGLt3y4gJXUzTTg3Bs79tPzZqauRd8GHC7XcHoyqujsY9NyDTVLsUmJ7dkA4eUwMy4796LC6LaGcw2sAeAAAkB4IzAKQY10ELg7GidmoIjj9Llln3a9c7p+Hp2juDUa2uig+Tvf2m8ryp/2s7z2uqb0bd+7AkfbAnrDBTtgEASSz1fwMDQBsTuzCYZZqK5PdIsDWShdWps8LHn2jX3rlvS/WExfKwpTm7QoqNzV29hgp8qTvSfLDuXkOdY1YL2x+2tGQ/U7YBAMnL7XQDAIAjE3srqmjXbpLP52A3aIj/6ScOeS6am2s/dm3ZrMz/d7ei+QX2c1Xf/4E+2BOKu+1UB7ehwgxThpE+wdkwDPXLMFUZqbvF1tKKiHpnJOc9qQEAIDgDQCqJROQqLa0re/V2sBkcqfCQofLNetOu3SuWKxgTnL8uj2h7zGJgmaY0KCu9QvMBXtNQb7+popgp6e/sDmlkjkuuRrzfwdmuw24DAEBz4c+6AJBCzO3bZISCds31zakl2q27oh072bV75XL78Y6cTlpSVjdd2ZQ0OMsldxqG5gNyvWbclO2aqFRSw0JhAIDkQ3AGgBRy8MJgrKidYgxD4SFD7dIs2SijvFwh06W5x5yi2Cue+2aYKXnbqSNVmGEqJjtrS8DS/jALhQEAkgvBGQBSSOz1zZbbo2i37g52g6MRPqYuOBuWJdeqlVrQ/zjty2pnP9/Lb6qrN/1DsyR5zNrrnWOtrYoowirbAIAkQnAGgBQSO+IcLSiQXFznmWoihf1lebx2HVi/Xst7DLDrDFM6raM7La9rbkiu11TuQVO2N1YzZRsAkDwIzgCQKkJBmVu32CXTtFOUx6PwwIF2mbFimcyY69ZP7+huE1O0D9bvoCnbW4OWypiyDQBIEgRnAEgR5ubNMqJ1o3CsqJ26IjHXOfsqy9Xzi88kSUOyTPXMaJuzCDymocLM+K8lG6sjspiyDQBIAgRnAEgRLAyWPsJDjomrC+fPUcfK/Tqxfdu+S2Rnj6lOMcPOZRFpL6POAIAkQHAGgBQRG5ytjAxZnXMd7AZNUZHXTdsHD7Pr/vPn6OzlH8tttr0p2gfr7T941DnKqDMAwHEEZwBIEbHBOdKjp2Tyv/BU9WnhCK05fYJddypZry7FRQ52lDwyXUbciuJVUWlniOAMAHAW37oAIBVUV8ncucMumaadura166xV3Qu19lvnxD3vXrHcoY6ST0+/GfcFZWN1VFFGnQEADiI4A0AKcJVuiqujLAyWkixJ8wceL0naNmSEKjrn2a+5VxKcD/CZhrr76kadg5a0NUBwBgA4h+AMACnAVcLCYKlqWyBq//NpXl/taNe59gXT1LZRJ9nbmcXrtXpnuVZVRLSqIuJQt8mjh8+UO+aS79JAVOEo4RkA4AyCMwCkAHNzqf042q6drPYdHOwGRyPo9mj5wFF2bUbCcnftElNH1O7zT5xoLSm5TUM9fHVfU8KWtDkQTbAHAAAth+AMACnAtWWz/Tia38PBTnC0Vg4YroDPb9eD1q84ZMp9x4/eb+Wuklt3n6GYdcK0JWApwKgzAMABBGcASHYVFTJ277LLSH6+g83gaOzPbq91vQbZdWZ1hQauX65Ido4C3er+fXZY8L4D3SUv0zDUK+b2VFFJm2oYdQYAtD6CMwAkOffK5TJiVhRmxDm1WJK+GnK8rJjbhw1fuVjuaO11zFX9B9rPZ69cJk/M6umQungNZcZ8W9ketFQVYdQZANC6CM4AkOTcy5bG1Yw4p5bN3XppZ253u87btVX52+tWSa/qPyhu+w4ff9BqvaUCwzDUOyP+6wqjzgCA1kZwBoAk516+zH5sebyycvMSbI1kEjZdWjr4OLs2olGNXLlIMZftqrp3P0VdLrvusIDgfLCObkPt6n5E2hWyVBZm1BkA0HoIzgCQ5NzLv7YfR7t3l0z+150qlhX0V1VGtl0XbixSu4r9cdtYXq9qeve1644L5kkWoTCWYRjq4Y//3C8vDzvUDQCgLXI73QAAIIFIRO6VK+rK/AIHm8GRCJkuLel1jF17gwENWft1vdtW9R+kzPVrJUm+rVvU88F7FcrrethzbLv8B83Sayro4K691rnqm1naRVVRjWpnye8yEu8IAEAzYNgCAJKYq3i9jKoqu44SnFPGsoIBqvZl2PWA4hXyhkP1bltVODCuzlxb1KK9pSLDMFQQM+ocsaSVlREHOwIAtCUEZwBIYu7lBy8MRnBOBSHTpS96x44216hwY8NhOJDfQ5GMTLvOXLumRftLVbme+Ps6r6iIKMx9nQEArYDgDABJLHZFbcswFO3WPcHWSBZLewxUtddv1wOLV8gTSXBNrmmqqnCAXWYUr5EijKYezDQM5fvqvrrURKU1VaywDQBoeVzjDABJzBUz4mx17iz5/Qm2RkvaFmhcQAu73Frca4hd1442rz7sflWFA5Wz7CtJkisQkL90o2p69zu6ZtNYV5+hTTXSgT8rLC0Pa1CWKdPgWmcAQMthxBkAkljsiHMkv4eDnaCx1vUepGDsaPP6FXInGm3+xsH3c85ce/iw3Ra5DUPdfHUhuTwibaxm1BkA0LIIzgCQpIxdu+TattWuo/n5DnaDxgi53Frdt+7aZl+gWoUljVvoK9yps4Kdcu06cx3BuSH5PjPuC8zX5RFZ3MILANCCCM4AkKRYGCz11I42++y6drS58dcqV8dc5+zftFFmTU2z9pcuvKah/pl1X2F2hSxtCxKcAQAth+AMAEnKvXxZXM2tqJJbyO05ZLS5X8mRjRrHTtc2olFlFK9ttv7SzbAcV1y9tJzF1AAALYfgDABJyr3sa/uxlZkpq30HB7vB4aztPVihmNHmQeuXyx09sjBXVThAVswiV0zXblgHj6leMfd13lQT1d4Q1zoDAFoGwRkAklTsiHOke4HEqsFJK+j2aE3fwXbtr6lSv5IjvxdzNCNTgYKedp25tnHXR7dVjDoDAFoLwRkAklEgINeautDEwmDJbV3vQQp54kebXUc42nxA7HRt784dcu3f1+T+0lVXr6Eu3ro/KK2riqo6wrXOAIDmR3AGgCTkXr1KRrjuFkbciip5RUxT63rXhV1/TZX6bjry0eYDqmIWCJOkjA3rj/pY6c4wjLhR56ik1ZWMOgMAmh/BGQCSkOuQhcEYcU5WJfl9FfBl2PWA4pVyRY/+WtuaHr1luerCIME5sV5+U5kxM7ZXVUYU5dZUAIBmRnAGgCQUtzCYx6No124OdoOGWJLW9Bli1+5wUH1Lm7YStuX1qibmOueMDeuadLx0ZxqGBmXVJeeKiLS5hkXCAADNi+AMAEkobmGwAYMkt9vBbtCQ7bn5Ks+pW+28z6Z18oRDTT5udZ9+9mPfjm0yKyuafMx0NijLpdil81ZVEpwBAM2Lb2IAkGwsS+5lS+0yfOwwB5tBIqv71o02G9Go+m9Y1SzHre5TKH34nl1nbCxW5TF8DmKtqoi/lrmjx9CeUO0U7ZKaqL4qC8tnHroS/eBs1yHPAQBwOIw4A0CSMTeXyoxZSTk8lMCUjPbldNTO3O52XbCtRFk1lc1y7JrefePu58x07cPr7o0PydsCjDoDAJoPwRkAkkzsaLPEiHOyWhMz2ixJAzasbLZjR/0ZCnSrWxAuo5jgfDjt3Yb8Md9qtgctFgkDADQbgjMAJBn38oOC89BjHeoEDan2ZWhT9z52nbtnuzrt392854i9znnrZhmBmmY9froxDEPdvHVfa0KW7KnbAAA0FcEZAJJM7IhzJL9AVqfODnaD+qzrPUiWWfcrdEBx8402H1DTp9B+bFiWMko2NPs50k0XrxG3SNi2IMEZANA8CM4AkGRiR5wZbU4+YZdb63sNsOvsyjJ131Ha7OeJHXGWmK7dGB7TUK6nLjrvD1uqihCeAQBNR3AGgCRiVJTLtaHYrlkYLPls6FGokMdn1/03rNShazc3XSQ7R8G8LnbNAmGN080X/9Vme5BFwgAATUdwBoAk4lq5Iq6OMOKcVCwZWttnsF17gwH1Ll3fYuerjpmu7SstkREKtti50kWOS8qM+XazI2gpwiJhAIAmIjgDQBJxr1geV4ePITgnky1de6gyM8eu+5WsljsaSbBH08RO1zYjEflLS1rsXOnCMIy4UeewJe1mkTAAQBMRnAEgibhXLLMfW36/In37JdgarS32FlRmNKLCjUUter7YEWdJ8m9oudHtdJLnNeK+4HBPZwBAUxGcASCJxI44hwcNkdxuB7tBrL3tOml3x7prjntuKZY/2LK3iAp36KhQh452zXXOjeM2DOV56648L49IFWFGnQEAR4/gDADJwrLirnEOHzPUwWZwsPW9BsbV/TesapXzxo46Z5RskCItNzU8ncTe01mSdrBIGACgCQjOAJAkzM2lMsv223WE4Jw0atwebcrvY9ed9+xQh/J9rXLu2OBsBoPybWn+W1+lo2y3oWxXXb0zZCnKImEAgKNEcAaAJBF7fbPEwmDJZFX3QkVcddPmC0tWt9q5D7mfM9c5N1oXb/wiYXtZJAwAcJQIzgCQJA5ZUXsII87JwJK0tGCAXfsC1crf3nqrW4dy8xTOyrZrrnNuvFyPEXeP7e1BgjMA4OgQnAEgSbhiRpwjXbvJys11sBscUNKpu8pibkHVd9NauaKteL2sYai6b8x1zhuLpdY8fwrzmIY6eeqi896wpeoI4RkAcOQIzgCQJGJHnLm+OXks6xGzKJgVVd9Na1q9h9jp2q7qKnl3bGv1HlJVF68RV6+tYnE1AMCRIzgDQDKoqZFrbV0g4/rm5FDmz9KGzvl2nb+9VJk1Va3ex8H3c+Y658br6DYUM+isNZVRWSwSBgA4QgRnAEgC7tWrZMRMv+VWVMlheUF/yahLXa25KFisYNfuivj9du0vKXakj1RkGEbcqPPesKXdLBIGADhCBGcASAKugxcGY8TZcWHT1IrudSO9ORX7lbfboSnSpqmann3sMqNkgzN9pKguB93TeU0l07UBAEeG4AwAScC9vG5hMMvtVmTAwARbozWs7dJbNd66Ud5+JatlJNi+pdX06mM/9uzdI1fMPb+RWKYr/p7O66qjijBdGwBwBAjOAJAE4hYGGzBI8nod7AZS/C2o3OGQem929rri6pjgLDHqfKRiR50DUamkmpXJAQCNR3AGAKdZltwrltol1zc7b0dOJ+1oX3c7sEHbN8gTDjnYkRTo0VtWzPXWfoLzEck76J7Oa6oIzgCAxiM4A4DDjB07ZO7ebddc5VCaewAAIABJREFU3+y8pT0GxNXDSp1ZFCxW1O9XsGt3u2aBsCPjPuiezqU1UVVxT2cAQCMRnAHAYe4Vy+Lq8FBGnJ1U4/ZqTZfedt193w51rkyO64ljp2v7tpTKCNQ410wK6hqzurYlaR33dAYANBLBGQAc5j5oRe0II86OWtW9nyIut10nw2jzAbELhJmRiLKXfeVcMymog9tQZsw3H+7pDABoLIIzADgsdsQ52qmTol27OdhN22ZJWp7f364zAtXqt7PUuYYOUtOrb1zdbslChzpJTYZhqDCzbnlt7ukMAGgsgjMA/H/27jw4rvLKG//3ubc3Sa1dsqSWZEmWvGKwjTPxhCWAycDged/Ayyw4IUVlqDdUJiRMhWQm+SPJ1Pg3SaYShkxISOXNLCGU7QzFTMIMCSFsZt+8YcC2bMnWvu9L733v/f1h+y6yJMtqSU8v308VVX26b5sjIeR7+jnPeSSzrzgnNm0GhMxDj7JbT9EqjOcVmPGmvjNQjdQZIhUvKUUiz2/GBYdZOF+utXnOW5/TPNOZiIgWgIUzEZFM8TjU081myInach23HUEFw8Cm3jPykpmNEI5V54Ij7wJsNb4sxW4F5bYhYW1hHTq/h0REdAksnImIJFJbWyDi1jFH3N8sT8jtw9nyWjOuG+lFQSQoMaPZhevqzcee4UH4ujrkJZOmmvKsdu2Ifm7CNhER0Xxcl76EiIiWy0UTtbniLM3JwBroivV58hW9rRKzmZt9QBgA5B9556LnaG7N0xoSunOF+eikhksN2N7gV+e/gIiIMhpXnImIJLLvbzYUBYl1GyRmk710CMdQMH8kiLqRXokZzS0aqIWhWkUc9zlfPo8iUOyy2rVH4wY0tmsTEdE8uOJMRCSB7/GfAwA8zz9rPqeXlsH3n0/ISimrdZVUYirHGrq1qfcMlBQtpAy3G5FADXLOt2gXHD0oOaP0VOYRGEuc+2+sAxiJG1jl4WA+IiKaHVeciYgkUvqsVU29KiAxk+x2vNpabRa6jo2pNhRsBntrdl7zcajTU/KSSVOlbuG4CRqKpeYHJURElBpYOBMRyRIMQpmYMEM9wMJZhmlvDtrLqs24YbgH/lhYYkaXZi+cha4j/9gRecmkKVUIlNima48nDMR0Fs9ERDQ7Fs5ERJKofc49tBpXnKU4EWiCIexDwVokZrMwYduRVACQf4T7nBejfEZr9kichTMREc2OhTMRkSRKX48j1itZOK80XQicCDSacUF4CrWj/RIzWhitoBDxomIzLjjyjsRs0leRS8A2IwxDMR5LRUREs1uSwjmRSOCxxx7Drl27cNVVV+Hmm2/Go48+irjtbNL5jI+PY8+ePdi5cye2bNmCO++8E88888ys1/b29uJrX/sabrzxRvPaJ598EkaKDnEhIpqL2mMVzoYvB0ZJicRsslN7aTWC3lwzvqKnFekyHipiW3UuOHIQ0Fn0XS5FCJTZ2rWnNCCs8X6CiIgutiSF8549e/Dd734XRUVFuOeee1BRUYFHHnkEX/nKVy753lAohHvvvRf79+/Hli1bcPfdd2NychJf/vKXsXfvXse1/f39+PM//3P85je/wdVXX41Pf/rTiMfj+MY3voE9e/YsxZdCRLRilF6rcNYC1YBIl5Itc9iHgim6hg19ZyVmc3nCtn3OrqlJ5LaekpdMGiv3OG+FhtmuTUREs0j6OKojR47giSeewK233oof/vCHEELAMAx8/etfx1NPPYUDBw7gpptumvP9jz/+OI4fP45vfetbuPvuuwEAX/jCF7B792489NBDuO2221BaWgoA+P73v4/h4WH85Cc/wc033wwA+MpXvoJ77rkH+/fvx6c+9SmsW7cu2S+JiGj5JRJQBqyWYL26ep6LaTlM+PLQWVJlxo1DXciNRyVmdHnsA8IAIP/oQYTWbZSTTBrLVwGvAkTPL9gPxXTUeAUEP8giIiKbpFec9+3bBwD44he/aP4lI4TAgw8+CCEEnnzyyXnfv3//fpSVlWH37t3mc36/H5///OcRDofx9NNPAwAMw8DAwAA2b95sFs0A4HK58Md//McAgGPHjiX75RARrQhlcABC08yYg8FW3olAk2OV/4qeVonZXL5oZQBajtVmXnCYA8IWQwiBclu7dlgHgto8byAioqyUdOF86NAhFBcXX7TSW1FRgfr6ehw8eHDO93Z2dmJgYADbt2+HqqqO13bs2AEA5vuFENi7dy/+67/+66I/5+zZc611F1amiYhSndIzYzBYdY2kTLKTJhScDKwx4+LgBALjgxIzWgRVxdSWq82wgJO1F21mu/ZQnPvFiYjIKanCORaLob+/H6tXr5719erqakxOTmJ0dHTW1zs7OwFg1veXl5fD6/Wivb191vfquo7+/n48+uijeOKJJ7Bp0yZ8/OMfX9wXQkS0wtTebvOxoSjQKyolZpN92suqEfbkmPGm3vQZCmY3efVHzce5Z1vgGh2RmE36ylUF8myf3w/FDA4dJSIih6T2OI+PjwMA8vPzZ339wvNTU1MomWVa7IX3FxQUzPp+v9+PqampWV/72te+hv/5n/8BADQ0NOBf/uVf4HIlt2W7vHz2r4OSw+8rZaKkf64Hrf3NIhBAfrE/yYzocpxabXVJqbqGj0z1ISffN+97xsXCTopYSfFrrwN+8rAZV5w6hslbdiX1Z+Zf4vuQqWqMGE5NxAAAcQOIedwo81n3FeVl2fl9yRS8F6FMxJ/rlZXUinMikQAAeDyeWV+/8Hw0OvuwlYW8f673rl27Fvfeey+uu+46tLW14dOf/jS6u7tnvZaIKKUYBtDVZcU1bNNeSePeXLQVVpjx+rEe5GgxiRktXvAjH3XEee++LSmT9FeV6/zwvS+YkJQJERGloqSWaH2+c5++znVecyx27kYkJydn1te9Xq/jutnen5ubO+tr9913n/l437592LNnD/bs2YOf/exnC0t+FkNDs69u0+Jc+BSM31fKJEvxc610d6E0FDLjyKpKxKciSedGC/Nu+XpHvK7jNKYW8P2PRFNv3+u4Ow+hxnXIPXMaAOB9+80FfS2zubDSvNj3Z4JCl8BE4lyLdn8ogVp3GOr5AXJDRup1HNCl8V6EMhF/rpOz2JX6pFac/X4/FEXB9PT0rK9faLOeq5W7sLAQAOZ8//T0NPz+S7cv3n333airq8Nrr702ZxFORJQqXB9+4Ij1AFecV0pvzMDxKmsomH96EspAP/qj+iX/SVX2fc757x+FmOPDbLo0+3RtDcAYz3QmIqLzkiqcPR4PAoHAnC3S3d3dKC4uRlFR0ayv19fXm9fNNDg4iGg0ioaGBgBAKBTCK6+8giNHjsz6ZwUCAei6jomJiUV8JUREK8f14fuOWAvwKKqV0reqBlGv1QXV0NWSlkPB7Cav/gPzsRoOIa/5uMRs0lupWzh+HoZiLJyJiOicpI+j2r59O4aGhtDW1uZ4fmBgAB0dHdi6deuc7w0EAggEAjh8+DB03flp/rvvnjtWY9u2bQDOrV7fd999+Id/+IeL/pxEIoHW1lb4/X4UFxcn+yURES0r+4qzXlQE5OZJzCa7tNWuNR8ruoa6nrMSs1kaU1c79znzWKrFcykCJbZV57GEgYTO4pmIiJagcL7jjjsAAD/4wQ/M4tcwDDz88MMwDAN33XXXvO//5Cc/if7+fuzdu9d8bnp6Gj/96U/h8/lw++23Azh3LvS2bdtw/Phx/Pa3vzWvNQwD//zP/4yhoSHccccdSU/WJiJabq7jtsKZbdorZtKXh4GyKjMO9HfBG599AGU6CTWuQ7yg0IzzD78jMZv0Z2/XNgAMs12biIiQ5HAwALjmmmuwa9cuPPPMM7jrrruwY8cOHD16FIcOHcKtt96KG2+80bz2Rz/6EQDgS1/6kvnc5z73OTz77LP49re/jYMHD6K2thbPPfccurq68M1vftNxjNXf//3f4+6778ZXv/pVPPvss6iursaRI0dw7NgxbN68GQ8++GCyXw4R0bISkxNQO9rNWAtUy0smy5wINALCKooaulokZrOEFAVT2/4AJa+8AIArzskqdguoOLfHGQCGYjoqvUmvMxARUZpbkr8Jvve97+GBBx7A2NgYfvGLX2B4eBgPPPAAHnroIQjbTcqPf/xj/PjHP3a81+/3Y9++ffjTP/1THDp0CPv370dBQQEefvhhfOYzn3Fcu379evznf/4nbrvtNrz77rvYu3cvJiYmcP/992Pv3r3Iy2O7IxGlNtcJ5/5TvZqF80rQhMBJ21CwvOAkykcHJGa0tCa37zAf+3q74enrkZhNelOEQKnHuneZ1IAo27WJiLLekvQ1u91u3H///bj//vvnve7UqVOzPl9WVobvfOc7C/p31dfX4+GHH77sHImIUoF60WAwFs4roaO0GiGvdbxhQ1dr2g8Fs5ucuc/56EEMV/Fna7HK3QKDtsFgHBJGRETsPSIiWkH2wWCGzwejuGSeq2mpHA80mY+FrqG+54zEbJbe1JarYSjWX+n5Rw5KzCb9FboEbIvOGIql7nFkRES0Mlg4ExGtIHvhrFVVAwp/DS+3SV8uOkutoWDV/V3wxtJ/KJidnudHcONmM+Y+5+QIIVBmq5xDOjAaZ/FMRJTNeMdGRLRS4nG4mk+Yoc7zm1fEyaqmzBwKNsPkNus8Z//xY1AiYYnZpL9yt/MW6UyIhTMRUTZj4UxEtELU1haIWMyMde5vXna6EDgRyNyhYHb2fc5KPA7/B+9JzCb95alAju0u6UxIg2FwrzMRUbZi4UxEtEJcMweDVfMM5+XWXhrI6KFgdvbJ2gDbtZMlhEC5x7pNCmrAAIeEERFlLRbOREQrxDEYTFGgV1RKzCY7nLANBVN0DXUZNhTMLlqzGrHyCjPOZ+GctHK382OWMyFtjiuJiCjTsXAmIloh9sJZX1UBuN0Ss8l8U95cdJRa+8gbhrrhy7ChYA5CONq1C468C7C1OCk+VSBfteK2sA6N31MioqzEwpmIaCUYBlzHrVZt7m9eficCjY6hYFf0tkrMZmVMbrcKZ8/IMHwdbRKzyQz2du2oDnRHOCSMiCgbsXAmIloBSl8vlNFRM9ZYOC8rXQicrGo044LwFGrGMnMomJ19xRkACo6yXTtZZW7h2BfP6dpERNmJhTMR0QqYORiMK87Lq6MkgKDPGgq2qfdMxg4Fs5u+Ygt0j8eMCw6zcE6WWxEoclk/PZ1hHTGd7dpERNnGJTsBIqJs4Dr+oSNm4Zyc/uj8q35HbKvNQtdR2t6K/ljmrxQaXi+mrtyKwvMFMweELY1yj8BY4lyxrAHoCOtYm6fO/yYiIsooXHEmIloBrg+sFWetKgDD75eYTWYL+XLRX24NBQsMdsEXi0jMaGVNbbPatfNOnYA6NSkxm8xQ4hawLTpzujYRURZi4UxEtAJcx46ajxNXXiUxk8zXXtMICOuvt4auzB8KZmff5ywMA/lHD0rMJjOoQqAux/qZ6o0aCGls1yYiyiYsnImIlpkYHoba1WnGia1XS8wms+lCoL3GOrs5NzSNVcN9EjNaeZPbdzjiwsPvSMokszTlWq3ZBoCzXHUmIsoqLJyJiJaZ6/2jjjixdZukTDLfQFkA4Zw8M27oasmKoWB28fJVCNXbJoofeltiNpkj4BXw2e6aOF2biCi7sHAmIlpm7qNHHHF8C1ecl0tb7VrzsdB11PWclZiNPJMf+UPzcf7RQxDxuMRsMoMiBNbYVp2H4wYm4iyeiYiyBQtnIqJlZt/frNXUwigvl5hN5gr5ctG3yhoKVjXYjZxoWGJG8tgLZzUSRt6J9+e5mhaqMcd528RVZyKi7MHCmYhombnesw0G28I27eXScdFQsBaJ2chlL5wBoPAg27WXQrlHIN92CtWZsA7D4JAwIqJswMKZiGgZKf19UPut4VRx7m9eFgYE2mYMBasY7peYkVzhhkbESsvMmPucl4YQAo22du3JhIHhOAtnIqJs4JKdABFRJnMde88Rc8V5eQyUVTmGgtV3t0Ig8wuayl8+NudrsYoqeEaGAQBFb7yMyv0/R/+n/3KFMstcjbkK3puyJmq3hnSUe7gOQUSU6fibnohoGbnecw4GS2zZKimTzNZeY02RhqGjvvuMvGRSRLhujfnYFZyGe2RIYjaZo8itoMxtzWo/G9Kgs12biCjjsXAmIlpG9sJZq6uHUVwiMZvMFHN70FdRY8aVQ31ZOxTMLlzX4Ihz2tskZZJ5GnOt26eIDvRGWTgTEWU6Fs5ERMvFMOC2TdSOb+MxVMuhs6oeumLtO63r4WozAEQDNdDdbjP2dWTn0VzLYU2u6jgf/ExIm/NaIiLKDCyciYiWidLTDWV42IwTPL95WXTY2rQ9sSiqBrslZpNCVBWR2jozzOngivNSyVUFqrxW6dwR1pHQuepMRJTJWDgTES0T+zFUAJDgRO0lN5FfhPHCUjOu7W2DqvNs3Qvs+5w9I0NwDw1KzCaz2Kdrxw2gM8KfOyKiTMbCmYhomdjbtAEgcdUWSZlkrvbqRkdczzZth8iMfc4Fh9+RlEnmqc9RYDvSGWdCLJyJiDIZC2ciomViHwyWaFoLI79AYjaZRxcKOqutwrBwchRFk2MSM0o9kdp6GMJqKeZ5zkvHowiszrFuo7oiOiIa27WJiDIVC2ciouVgGHDZVpx5fvPS6ysPIObxmXEdj6C6iO7zIVoZMONCFs5Lyj5d2wDQFuaqMxFRpmLhTES0DJT2Nijj42bM/c1Lzz4UTOgaVve2y0smhUXqrX3O/uPHoISCErPJLDU+BR7beO1WTtcmIspYLJyJiJbBzP3N8a3bJWWSmSIeH/rLq824arAH3nhUYkapy36es9A05L93WGI2mUUVAg22VefBmIHxOFediYgyEQtnIqJlYJ+obSgKEpuvlJhN5ukMNMBQrL/C6tmmPafwaueAMLZrL611uaojbuGQMCKijMTCmYhoGdj3N2vrNwB5eRKzySwGnG3avkgYFcO98hJKcVphEeLFJWbMAWFLq9wjUOiy+rVbgxp0g0PCiIgyjUt2AkREmcD3+M+tQNfhPvSuGRp5fufrlJTB/BJM5heZ8eres1BYqMwrXLcG7rFRAED+0YNAIgG4eAuwFIQQWJen4ODEuf3NIR3oieiozVEv8U4iIkonXHEmIlpiYngIImrtt9VqV0vMJvM0V61xxHU9ZyVlkj7s+5xdwWnkNR+XmE3macpVYZsRxnZtIqIMxMKZiGiJqV2djlirqZWUSeZJKApaKurNuHh8GAXTE/ISShP2ydoAUHjwLUmZZKZcVaDGZ91SdYR5pjMRUaZh4UxEtMTU7i7zsaEo0AOBea6my9FWVoOo22PGHAq2MLGyVdBycs248N03JWaTmdblWbdUOoAzPNOZiCijsHAmIlpiim3FWa+sAmyFHiWnudJaOVW0BGr62qXlklYUBWHbqnPhwTcB7gtfUrU+BbZFZ7QEeaYzEVEmYeFMRLSUNA1qT48Vcn/zkgm5vegqqTTjwEA3PIm4xIzSS7jBmkTuHh1BbkuzxGwyjyoEGm1HU43EDYzEuOpMRJQpWDgTES0hpb8PIh4zY52F85JprahznN28urdNYjbpJ1zf6IgL33lDUiaZa22u87aKQ8KIiDIHC2cioiWktjuLOa2+YY4r6XKdtg0F88QiPLv5MkWrqpHILzBj7nNeeqUeBaVu25nOIQ0aW+KJiDICC2cioiWktltHIxm+HOirKiRmkznGc/IxUFhmxjV9HTy7+XIpCiY+8odmWPjOG9znvAzW5Vnt2lEd6OSQMCKijMDCmYhoCant7eZjra4eUPhrdimcrqhzxGzTXpyJHdeajz3Dg8hpa5WYTWZqzFUcN1ds1yYiygy8oyMiWiJiYhzK2KgZs017aRgATlda38uC8BRKxoflJZTG7IUzwH3Oy8GrCNTlWLdX3REdIZ7pTESU9lg4ExEtEftqMwBo9fVS8sg0g/mlmMjNN+N1/e0Q81xPc5u+YgsSeX4zZuG8POxDwgyc2+tMRETpzSU7ASKiTKF2WO3DhhDQauvmuZoW6nRlvSNeN9COqJxU0p/LhcntO1Dy6osAzg8IMwxA8KOIS2meXnjxaxgGPAKInV9oPh3UcaXfgOD3mYgobXHFmYhoidgnautVAcDnk5hNZtCFQEuFdaTXqskRFIemJGaU/iY+eo352NvfC19nu7xkMpQQAuUeq0ieSBgYjLFdm4gonbFwJiJaCvEYlJ5uM+T+5qXRVVyJsCfHjNf1t8tLJkNwn/PKqPA4b7Gag2zXJiJKZyyciYiWgNrdBaFZN8YsnJeGvU1bGDqaBjvkJZMhpq/cBs1nfRhR+C4L5+WQowoUuqxV57aQjqjOVWcionTFwpmIaAlcNBisjoVzsmKqC2fLa824drQfebGIxIwyg+HxYPLqj5oxV5yXT4WtXVsD0MpVZyKitMXCmYhoCSj2/c35BTBKSiRmkxnaymqQUK0ZlusG2uUlk2Hs7dq+ni54ezolZpO5St0CPtudVnNQh2Fw1ZmIKB2xcCYiSpZhOAaDafUNnFK8BE5X1JuPXVoCDUPdc19Ml2VixzWOuPCdNyVlktkUIbA2VzXjcQ4JIyJKWyyciYiSpLSdhRKcNmPub05eyO1FV0mlGTcMdcOjJSRmlFmmtmyH7vGaMdu1l8/6PA4JIyLKBCyciYiS5D74jiNm4Zy81oo6GIr1VxTbtJeW4fVhcttHzJgDwpZPoVtBlZdDwoiI0h0LZyKiJLkPvms+Nlwu6NU1ErPJDPY2bV8sgtrRPnnJZKhJ23nOOR1tcPf1SMwms63Ps9q1OSSMiCg9sXAmIkqSfcVZq6kFXK55ria7/qh+0T+tSi4GCsvMa6p72zEU0RzXUPImPuo8zznvzdckZZL56nMUDgkjIkpzvLsjIkqCmJyA2nzCjHW2aSetu6rOEdf2tUvJI9NNXv0H0N1uKPE4AMD/5usY/9PdkrPKPM3T51aXS9wCvdFzxfJ4wsDBCQ0FrvmHCG7wq/O+TkREK4eFMxFRElyHD0HYVo64vzl53VX15uPc8DRKxoflJZNhKn/5mCOOVlUjp7MdAFD4u98gvGUb8iJx8/X+T3125ZLLcJUeBb1Rq0V7IKqjwMXCmIgoXbBVm4goCRcNBqurl5NIhpjMK8BEQbEZ1/R1gAd7LZ9wQ5P52D00AGViXGI2mS1HFSi0rTAPxw0kOCSMiChtsHAmIkqCvXDWS8tg5BdIzCb92VebgXOFMy2f0JomR+w70yIpk+xQ4bEKZx3AYJyFMxFRumDhTES0WJoG1+FDVsg27aQYALps+5vzgpMomhyVl1AWiKxugK5a7cLeltMSs8l8pW4B+7bmgSiHhBERpQsWzkREi6Q2n4QyPWXGbNNOzkR+Mab9hWZcyzbtZWd4PIjU1psxV5yXlyIEVtlWnUM6MMWTqYiI0gILZyKiRXK//aYj5opzcro4TVuKsK1d2zUyDNcYV/mXU6XHeevF49WIiNIDC2ciokXyvPm6+djIyYFeWSUxm/RmwLm/uWBqHAXTE9LyySahNWsdcU5bq6RMssNsQ8LiHBJGRJTyWDgTES2GrsP95mtmmFjTBCj8lbpYY4WlCOX6zbimr11aLtkmWlsH3e0241y2ay+7Slu7tgFgMMbCmYgo1fEuj4hoEdRTzVBGRsxYa2ya52q6lC5O05bGcLkQWW1tM8hpawU4sGpZlbgF3PYhYTEOCSMiSnUsnImIFsH9xquOWGtaO8eVdCnn2rSt/c1FEyPID03N/QZacqFG6+fXPTEO9+iwxGwy38whYWEdmEywcCYiSmUsnImIFsHzhrW/WS8u5v7mJAwXr0LEl2vGXG1eeeEGZ8dEDtu1l91FQ8LYrk1ElNJYOBMRXa4Z+5vjH7uO+5uT0D1jmjYL55UXqa6F7vWZcS4HhC07nypQZBsSNhI3EOOQMCKilMU7PSKiy/Xhh1DGxswwfu11EpNJb7oQjsK5dGwQeZGgxIyylKoiuqbRDHPOcp/zSqj0ckgYEVG6YOFMRHS5DhxwhLFrPy4pkfQ3VFqJmMda6eRqszzRpnXmY9f0FDxDAxKzyQ4lLgHbVmf0c0gYEVHKYuFMRHS5Xn7ZfKiXlEDbsFFeLmmuu9LWpm0YqO7vlJdMlovYCmeA+5xXghACFbbKOaoD4xwSRkSUklg4ExFdDl0HXnnFDOPXXM/9zYukCQU9lbVmXD46gJxoWGJG2S0eqIaWYw1p4z7nlVHh5ZAwIqJ0wLs9IqLLcewYYNvfHLv2eonJpLfOkirE3V4zrulrl5YLAVAURG3nkeecbT33QREtK68iUGIbEjYaNxDlkDAiopTDwpmI6HLY2rQBIM7CedFaKqw2baHrqO7vkpgNAc59zmo4hLzm4xKzyR72IWEAMMBVZyKilMPCmYjoctgGg+llZdDWb5CYTPqKKyray6rNeNVIH7zxqMSMCHAWzgBQ9OarkjLJLkUuAXvH9kCUQ8KIiFINC2ciooXSNOBVq5CIXXM9IMQ8b6C5dJQGEHe5zbiW07RTQryyCok8vxkXvv3aPFfTUhFCoNJj3ZLFDGCUQ8KIiFIKC2ciogVyffg+MDFhxmzTXrxWW5u2omsIDLBNOyUIgfAaa59z4btvAomExISyxyqPgP1juIEoC2ciolTCwpmIaIHcb7zuiFk4L05MdaG9NGDGFUO9cCfiEjMiu9CateZj1/QU8j98T2I22cOjCJS4rdJ5LGFgiqvOREQpg4UzEdECud+w2rT18lXQ1q6b52qaS1tZDTTVZca1fe3ScqGLhW2FMwAUcp/ziqn0OLd+nApqkjIhIqKZWDgTES1EIgH322+ZYeza67i/eZHsbdqqlkDVYI/EbGimeGkZ4oVFZlz8xivzXE1LqdAl4LPdmZ0KatA4JIyIKCWwcCYiWgDXB8egTE2acfzaj0vMJn1FXB50llSacdVgN1wa99DhKfhJAAAgAElEQVSmFCEQarS6KQoOvw0lHJKYUPaYOSQsogOdYZ6lTUSUClg4ExEtAPc3L42z5TXQFdWMazhNOyWFbcdSKbEYCg69IzGb7DJzSNhJtmsTEaUEFs5ERAvged3WrlpVBa2xae6LaU72Nm13Io7KIbZppyL7ijMAFL3Jdu2V4lYEymxDwvqiBsbjXHUmIpKNhTMR0aVEo3C/9YYV33QT9zcvQsjtRXdxhRmvGeqGqrMgSEWaPx/RiiozLuI+5xVV6XXenp0K8v8TIiLZWDgTEV2C+923IcJh64lbb5WXTBo7s2o1DGH9tdM0yDbtVBZqtKZr+48fg2tsVGI22SVfBXJtd2gtIQ0JDgkjIpKKhTMR0SV4Xn7J+cQnPiElj3TXYmvT9sajqB3tl5gNXYq9XVsYBoreek1iNtlFCOFYdY7qQHuIq85ERDKxcCYiugS3vXDevBkIBOQlk6amvTnoKyw348ahLqgGC4FUFm5ohO6yztsueuNleclkoXKPgMu2I6SZQ8KIiKRi4UxENA8xNAT3B8esJ265RV4yaax11WrHvvCmAbZppzrD68PUlu1mXPTmqxKzyT4uIbDG1q89EDMwyiFhRETSsHAmIpqH57WXnU+wcF6UllVWm3ZONIzqsUGJ2dBCjV97o/k4p+MsvN2d8pLJQhvzVEfcPM1VZyIiWVg4ExHNw76/2fB6get5fvPlmvDlYbCwzIybBjuhgIOO0sH4tTc4Yk7XXlllHsVxNFVrSEdc5/87REQysHAmIpqLYTj2N8d3XAPk5kpMKD3Zz24GzhXOlB6mtn4EiTy/GXOf88rb4LdWneMGcDbMdm0iIhlYOBMRzUE91Qy1v8+MYzfulJhNejIAnK6oN2N/JIiqiSFp+dDlMdxuTP7Bx8y46K1XAZ69vaLW5Chw24eEsV2biEiKJSmcE4kEHnvsMezatQtXXXUVbr75Zjz66KOIx+MLev/4+Dj27NmDnTt3YsuWLbjzzjvxzDPPzHpte3s7vvrVr+Laa6/F5s2bccMNN+Dv/u7vMDrK8yWJaGl5Xn7REbNwvnwj/iKM+ovMuGmgA2Ke6yn1jNn2OXtGhpF76oS8ZLKQWxFosg0JG44bGI7xwwsiopXmuvQll7Znzx488cQT2L59O3bu3IkjR47gkUcewalTp/DII4/M+95QKIR7770XJ06cwG233Yaqqio899xz+PKXv4zR0VF85jOfMa9tbW3F7t27EQwGsXPnTqxevRrHjx/Hf/zHf+D111/Hk08+iZKSkqX4koiIYN/frJeVQ9t0hbxk0tQp22ozAKzvb5eSBy3ezH3OxW+8gtDGzZKyyS4XVpe9ivPjpnfGE2jMVWd7CwBnezcRES2NpAvnI0eO4IknnsCtt96KH/7whxBCwDAMfP3rX8dTTz2FAwcO4Kabbprz/Y8//jiOHz+Ob33rW7j77rsBAF/4whewe/duPPTQQ7jttttQWloKAPjud7+Lqakp/OhHP8Ittsm2P/nJT/DDH/4QP/nJT/CNb3wj2S+JiAi+f/sZ3K9Zg5C01avh2/sLIN937vWpiKzU0oYOgZbKejMunR5DWXBcXkK0KKH1mxArLYdn5FyLfdEbL6Pn/94vOavskqcK+FXgQpf2UMxAfY4BVbB/g4hopSTdqr1v3z4AwBe/+EWI87/AhRB48MEHIYTAk08+Oe/79+/fj7KyMuzevdt8zu/34/Of/zzC4TCefvppAMD09DTeeustXHHFFY6iGQDuu+8+eL1evPoqz5gkoqWhtp+FsG03SazbIDGb9NRTvApBrzVMbR1Xm9OTEBi/5uNmWHjwLYhYTGJC2anSY92yaQCGY5yuTUS0kpIunA8dOoTi4mKsW7fO8XxFRQXq6+tx8ODBOd/b2dmJgYEBbN++HarqbCvasWMHAJjvNwwDf/M3f4O//Mu/vOjPUVUVqqoiFAol++UQEQEA1FOnHLG2br2kTNLXadtqMwwD6wbaZaVCSbKf56yGgsh/75C8ZLJUmUc4btoGuM+ZiGhFJdWqHYvF0N/fjy1btsz6enV1Ndra2jA6Ojrr3uPOznNHkqxevfqi18rLy+H1etHe3g4AyM/Pn7VoBoA33ngDoVBozjwWqrw8P6n30+z4faW0dOa09bi6Gv7qCsfL+edbtml2cUXF2VXW7/a6qSFUeQzA4/y+jYuFDZGklePzuc3HF37O47feAnzduqbi4OswbuawvJUWSETQHUwAAKY0AD438t0X72cuL+Pvp5l4L0KZiD/XKyupFefx8XN71fLzZ/+PduH5qamped9fUFAw6+t+v3/O914QDofxj//4jwCAv/iLv7h00kRElzI4CHR1WfGmTfJySVMtRVWIqVYBdsVI1zxXU6qL16xGZE2TGee/+tI8V9NyqfW7HXH3dEJSJkRE2SepFedE4twvbI/HM+vrF56PRqOLfn84HJ7z3x+LxfDXf/3XaGlpwc6dO7Fr164F5z6boaH5i3S6PBc+BeP3ldKN91dPw/5xXqi+Cdr5YWAXVuCmOBxsXscaqs3HqpZAoPMsprSLV5cjUbabpooLK82RiPXfyf5zPnrNDQicbQUA5B4+iHB3PxKFRaCVIwwDeQoQPP+/TU8wjoCqQ5kxJGzIYCfHBbwXoUzEn+vkLHalPqkVZ5/vfAvXHOc1x84PD8nJyZn1da/X67hutvfn5ubO+looFMJf/dVf4ZVXXsGVV16J73//+5eVOxHRXOzHUBkuF7Q1a+Qlk4bCbi86S6rMuGG4B95ZimZKL2Mft1qzha6j8E0O5FxpQghUeK1bt4QBjMQ5JIyIaCUkVTj7/X4oioLp6elZX7/QZj1XK3dhYSEAzPn+6elp+P3+i54fHR3FPffcg9dffx1bt27Fv//7v896HRHRZTMMuG2Fs9awBnDP3hVDs2tZVQdDsf56Wd/fJjEbWioTO66D7rIa1YpfZ7u2DOXumUPCWDgTEa2EpApnj8eDQCCA7u7uWV/v7u5GcXExiopmb+Wqr683r5tpcHAQ0WgUDQ0Njud7enrwqU99Ch988AGuu+46/PznP59zjzQR0eVST56AOtBvxhqPobpsp2zTtH2xCGpH++QlQ0tGyy/A1LY/MOPiV18CDBZtK82lCJS6rdbsiYSBsMb/DkREyy3p46i2b9+OoaEhtLU5VxQGBgbQ0dGBrVu3zvneQCCAQCCAw4cPQ9ed+9zeffddAMC2bdvM50ZHR3Hvvfeivb0du3btwk9/+tM5W7mJiBbD89ILjjixnoXz5RjPycdgYZkZrx3shMriKmPY27V9PV3IaTsjMZvsVel13r7xaCoiouWXdOF8xx13AAB+8IMfmMWvYRh4+OGHYRgG7rrrrnnf/8lPfhL9/f3Yu3ev+dz09DR++tOfwufz4fbbbzef/9a3voX29nbccsst+Kd/+ie43e7Z/kgiokXzHLAKZ72gEHpVQGI26aE/qpv/HCmrc7xW2nXW8frMfyi9jF3nPIKqiO3aUuSrQI7tDm4wZkDnB1RERMsqqanaAHDNNddg165deOaZZ3DXXXdhx44dOHr0KA4dOoRbb70VN954o3ntj370IwDAl770JfO5z33uc3j22Wfx7W9/GwcPHkRtbS2ee+45dHV14Zvf/KZ5/vPx48fx/PPPQwiBQCCARx999KJcvF4v7rvvvmS/JCLKVtPTcL/9phkmNmwAZkyrpbkZADqrre01ecFJlIwPy0uIltz05i2IFxXDPT4G4Fy7dt89/Ht3pQkhUOFR0B459+FT3ADG4gZKPfx9RUS0XJIunAHge9/7HpqamvDrX/8av/jFLxAIBPDAAw/gc5/7HITtpvPHP/4xAGfh7Pf7sW/fPjz88MM4cOAAXnvtNaxZswYPP/ww/uRP/sS87uDBgwDOrWY/9thjs+aRn5/PwpmIFs3zxmsQtlMCtPUbJWaTfkaLyhDMtYZBru5tA2/jM4yqYvzaG1H+218DAIrefh0iFoMxx7GStHxWeQQ6Iuc+sALODQkr5X8GIqJlsySFs9vtxv3334/7779/3utOnTo16/NlZWX4zne+M+97P/vZz+Kzn/3sYlMkIrokz0vPm48NIZBYu15iNumnM+Ac5ri6l9O0M9HY9TvNwlkNBVFw9CAmdlwrOavs41YEStzCPI5qLGEgqhvwKvy4iohoOSS9x5mIKCMYBjwv2vY319UDHD64YJqioLvK2t9cMjYEf2j2owYpvY1dd6MjLnr1RTmJECpntGYPcG4AEdGyYeFMRARAbTsDtbPdjBNs074sfatqEPP4zJirzZkrFqhBqMnqxih+7YDEbLJboUvAPmB7IGbA4JAwIqJlwcKZiAiAm8dQJaWtpsl8rGgaanvb5SVDy27semu6tv/4MbhHOAROhgtDwi6IGedatomIaOktyR5nIqJ0Zz+/WS8thV5TKzGb9BLMycNgWZUZV/d3wpOIScyIlkrlLx+b/QVNMx8Kw0Dd9/dgesvV6P/UZ1ckL7Ks8gh0Rqx4IMbCmYhoOXDFmYgoEoHnjdfMMHbDTkDhr8eF6qhudBzbVd/dKjEbWgnhhjXQVdWM81pnH/5Jy8+rCJS4rP//RuMGQhqLZyKipcY7QyLKeu6334QIh804tvMTErNJLzoE2msazTgvOIXy0QGJGdFKMDxeROrXmHFuyymAe2ulqfA6h4S1BLU5riQiosVi4UxEWc8zY39z7MabJWWSfrpKKhHOyTPj+u5Wnt2cJewDwlxTE/AM9kvMJrsVuwTsA7ZPBTUOCSMiWmIsnIko63kOWIVz/KqtMFatkphNejkRsFabha6jruesxGxoJQWbnAP0clvYri2LEAKrbJXzlAb0RVk4ExEtJRbORJTVlO4uuE41mzHbtBcu5PahvazGjCuHepATDc/zDsokscoqJPL8ZpzLfc5S2adrA+dWnYmIaOmwcCairOY58KIjjrNwXrDmqgbotiFqHAqWZRQFobVWu3ZO2xkoEX5wIotPFSiyDQlrD+uIcEgYEdGS4XFURJQVfI//fPbnf/Fv5mPD54N68gTU01w5uxQDwMkqq03bFwmhcqhXXkIkRWjtBhS8dxgAoCTiKHz7dYzd+EeSs8peFR6B8fPnOOsAWkMaNufzVo+IaClwxZmIspemwdVy2gwTa9cBtiN2aG59heUYzysw47ruM1A4jCjrhNZugGE7iqz4lRfmuZqWW4lbwO0YEqZzSBgR0RJh4UxEWUttb4OIRMxYW79RYjbp5aRtKBgA1PeckZQJyaTl+REN1JpxycssnGVSZgwJG08YGIyxcCYiWgosnIkoa6mnmx1xYv2GOa4ku6jqRuuq1WZcPtIPf2haYkYkU9D2gVNOx1n42vghikwcEkZEtDxYOBNR1rJP09ZWVcAoLpGYTfpoqahDQrX2TdZ3cShYNguuc3ZqlLBdW6ocVaDStup8NqwjpnPVmYgoWSyciSgrieA0lJ5uM9a42rxg9rObvfEoqgc6JWZDskWra6Hl5plx8SvPS8yGAGB9njWrQTOAMyFdYjZERJmBhTMRZSX19GkI29CcxDoWzgsx5C/GUEGpGa/rb4eq86Y8qykKgmut/3+K3nodSjgkMSGqz1VgW3RGc1DjkDAioiSxcCairOSy7W82VBXamsZ5rqYLPqxe64g39XI/KwEhW7u2Eoui8O3XJWZDLiHQZFt1Ho0bGI6zcCYiSgYLZyLKPoYB1b6/uWEN4PVKTCg9RF1unK6sN+OKiWGUBcflJUQpI7h2veNYqpKX2a4t24Y85y1e8zSHhBERJYOFMxFlHWWgH8rkhBnzGKqFaa5c4xgKdmX36Xmupmyi5/kRqbEmrRe/8gLA1mCpit0KKmz92mfCOqIcEkZEtGgsnIko61x0DNW69ZIySR8GgA9q1plxTiyCpkEOBSNLyLbPOaezHTk8lkq6DX4OCSMiWiosnIko69iPodL9fuhVAYnZpIeukipM5Oab8cbeM1AN3oSTJbh+kyMuZru2dPU5Cry2O72T0xwSRkS0WCyciSi7xONQz1grYdq6DYDCX4WX8oFtKJgwdGzuaZGYDaWiaKAGsdIyM+Z5zvK5hMDaXGvVeTxhYCDGwpmIaDF4t0hEWUVtOwuRiJtxguc3X9KkLw/tZdVmXD/cg/wojxuiGRQFY9fvNMPCd96AEgpKTIiAWYaEBTkkjIhoMVg4E1FWsbdpA4C2lvubL+iP6rP+805FI2CbmFzddsrxOtEFYzf+kflYiUVR9NZrErMhACh0Kwh4rf9/20M6IhpXnYmILhcLZyLKKvbBYFqgGkZBgcRsUp+mKGivbTJj//QEVo30S8yIUtnY9Tth2LY+FLNdOyVssJ3prAFoCXHVmYjocrFwJqKsISYnoPb1mrHGadqX1F1Vj5jHZ8aNnach5rmesluiuARTW7ebccnLz/NYqhSwOkdBju2Orzmoc0gYEdFlYuFMRFlDPX3KESfWcX/zpZxZbR1BpSbiqOs5KzEbSgejN1jt2r7uTuSc5SA52VQhsM626jyZMNAXZeFMRHQ5WDgTUdZw2dq0DbcbWsMaidmkvtHCUowVWVOSV/e2wW0brEY0m7EbPuGISw48JykTsltvK5wBDgkjIrpcLJyJKDvoOtRT1oqztqYJcLslJpT6ztpWmwGgsePUHFcSWaY3b0GsvMKMS176vcRs6IJ8l0CNz7rtaw/rCHNIGBHRgrFwJqKsoPT1QglOm3FiPfc3zyfq9qKrqt6My0YHUDg9IS8hSh+KgtGbbjHDwoNvwTUxLjEhusB+NJUB4DRXnYmIFoyFMxFlhYuOoVq3UVIm6aG9thG6arV2crWZLsfIzX9sPhaahuJXX5SYDV1Q61OQa+vYbg5qHBJGRLRALJyJKCuotsJZLyyEXlExz9XZTRfC0abti4QQGOiSmBGlm/Frb4Du8ZpxyYvPSsyGLlCEcOx1ntaArgjPYiciWgiX7ASIiJZdMAi13ZoGnVi/ARA8VGkufatqEMrxm/GarhYoXJWiy6Dn5mH8Y9ej5Pw5zsWvvAAkEoCLtx0roXl67hZsFYDAuVZtADg4oeHCsc4b/Ooc7yIiIq44E1HG87z1OoRm3UhqPIZqXq311vdH0TU0dPI4Ibp8oztvNR+7J8ZRcOQdidnQBV5FoMRtfXA4njAQ4pAwIqJLYuFMRBnPfcDaX2kIgcRaDgaby3h+MYZLrDb2mt52+GIRiRlRurIXzgBQynbtlBHwOm//+qNs1yYiuhQWzkSU8Twvv2Q+1mtqgbw8ecmkuNZ654cKTRwKRosUra7F9MbNZlzyEs9zThX5KmA/1nkgZiDB7RhERPNi4UxEGU3p7oKr5bQZJ9imPaeox4uuqgYzLh0dRPHkqMSMKN3ZV51zz5yGzzZrgOQRQqDKY90C6gAGYyyciYjmwykdRJTR7KvNAKCtY5v2XNpq1zqOoGrqaJ7naqKLVf7yMecTmnNIVf1D/x/Gr73BjPs/9dnlT4pmVeYRaI8AifP1cl9Uh2EYEBycSEQ0K644E1FGc9sKZ8PrhVbfMM/V2UsTAmdsR1DlhIM8goqSFq2uRSLPmtCe13xcYjZkpwqBCo9VJEd0oCfKVWciormwcCaizKVp8Lx6wAwTTWsBlcetzOZs+WpEfLlm3Nh5mkdQUfIUBcH1m8wwp/0MlEhYYkJkVzVjSNjxeY6xIiLKdiyciShjud47AmV83Ix5DNXcjtVaLeyKlkB9F4+goqUR3HCF+VjoOnJbuAUgVXgVgVLb0VTdER0TcU7YJiKaDQtnIspYM/c3J9azcJ7NQH4pBgrLzHh1bxu88ZjEjCiThJrWO/bO5zWfkJgNzTRz1flkkIUzEdFsWDgTUcZyHENVUgqjtGzui7PY+7XrHHFTO4+goqVjeL0Ir1lrxnmnTwI6i7NUUaACuba7wdNBDXGd2zSIiGZi4UxEGUlMTsB16F0zTqxbD3Ba7EWCHh9aV6024/KRfhROj8/zDqLLZ9/nrIaC8HW2y0uGHIQQjlXnuAG0hPjBBhHRTCyciSgjuV9/DcJ2FI7GNu1ZfVi9FrpitdE2tnP/KS09+z5ngNO1U025R8Bl+1zxxLQGg8MBiYgcWDgTUUbyvPyi+dhQ1XMTtclBEwqOVzeZcW5oGoHBHokZUaZKFJcgWlFlxv7mDyVmQzPNPJpqImGgl0dTERE5sHAmoozkOWAVzomrPwLk5M5zdXZqqahD2JNjxo0dpyDAm2VaHtMbN5uPPUODcA8NSMyGZqr0KrBvZvmQR1MRETmwcCaijKO0nYXa0W7GsZtulpdMijIAvF9jDQVzaQnUd7fKS4gyXnDTlY7Yf5KrzqnEpwjU5Vi3hd0RHWM8moqIyMTCmYgyjn21GQBiN+6UlEnq6i8sw1BBqRlv6DsLTyIuMSPKdNFADeKFRWacd+IDidnQbDb7VUf84RRXnYmILmDhTEQZx3EMVWEREluvlpdMinq/Zr0jvrL7tKRMKGsIgaCtXTunqwPuwX6JCdFMFV4Fq2x7nVtDOkIat28QEQEsnIko08TjcL/+qhV+/EbA5ZKXTwqa8ubiTHmtGdeO9KEkNCkxI8oW0zPatUtf+J2kTGgu9lVnHecmbBMREQtnIsow7sMHoUxPmXHshpskZpOaPqxeC0Oxfv1f1X1KYjaUTcL1jdB81kC60ud+KzEbmk1djoJ8W8d2c1BDXOeqMxERC2ciyiieF593xLGdn5CUSWpKKCpOBBrNuDA0ibqRXokZUVZRVQQ3bDLDordehTo5ITEhmkkRApvzrS6dqA60hDgkjIiIhTMRZRT3Sy+YjxPrN0CvqZ3n6uxzuqIOEY/PjK/qPu04goZouQU3Wu3aSjyOkldemOdqkmFtrgLbVmd8OJWAbnDVmYiyGwtnIsoYYmAA7g+OmXHsJq422xkA3q+1hoK5E3Fs6DsrLyHKSsG1G6Db5g6UPs927VTjVgQ22vY6T2lAZ4SrzkSU3Vg4E1HG8Lw84xgqtmk79Batwoi/2Iw39p2BR0tIzIiykeH1ItRkfYBT/PILENGoxIxoNhv9quMmkUdTEVG2Y+FMRBnDc8Bq+TRychD/w2skZpN6jtlWm2EYPIKKpLG3a7ump1D09msSs6HZ5KkCjbnWbeJAzMBglKvORJS9WDgTUWbQNHgOWCvOsWuvB3y+ed6QXSZ9eWgvqzbj+pFeFIWnJWZE2Sy48QoYwtpEy+naqWmzfbw2gA94NBURZTEWzkSUEVzvHYEyNmbGsZv/SGI2cvVH9Yv+ebtyLQxh/cqvOXvyomuIVoqW50ekrsGMS1/4HaDzZzDVlLgVVHutDzg6wjomExwSRkTZiYUzEWUEz0vOybwcDGZJqC601zaZcf7UOFaN9EvMiAiYtrVre4YGkP/eIYnZ0FyutB1NZQA4zlVnIspSLJyJKCPYC2etvgH6msZ5rs4uHdUNiLs9ZtzUcYpHUJF0wY2bHTHbtVNTwCtQ4rZ+Y5wOaohoXHUmouzDwpmI0p4YHYHr6GEz5jRtiwHgTN0GM3bHo1jdyyOoSL54aRmC6zeZcelzvwV4VnDKEUJgs+1oqoQBnOCqMxFlIRbORJT2PK8cgLDtj8zm/c0zDZZVYcpfaMYNXa1wabzppdQwcsufmI9z288g9/RJidnQXBpzFeTZ5oSdCGqI6/yQg4iyCwtnIkp79jZtw+NB7JrrJWaTWlptq80wdKzp4BFUlDqGb/3fjrjsmf+WlAnNRxHCsdc5qgOngvwAjoiyCwtnIkpvuu4onON/eC2QlycxodQxlZuP/lXWEVSBgW7kRYISMyJyCm7cjHDdGjMue/Z/JGZD81mfq8Bnu2v8cFqDxtZ6IsoiLJyJKK2pxz+EMjRoxtzfbDlTt94RN3U0S8qEaA5CYGjX7WaY19KM3Bb+nKYilyKwybbXOagBZ0I8QoyIsgcLZyJKa56XnnfELJzPibvc6KixJosXTo6ibHRwnncQyTF82+2OmO3aqWuTX4XLNpL//SkNBlediShLsHAmorTmOIaqugba+g3zXJ092qvXIOFymzGPoKJUFbziKoRX15tx2e+ekpcMzcurCGywTQmbSBjoiHDVmYiyg+vSlxARpSYxOQH3wXfMOLbzE4BgeTjzCCpPLILa3jZ5CRHNRwgM33Y7av/fDwEAeaebkdN6CuGm9Zd4Iy215gUcM+VTAIFzv2cA4J3xBMIJAxvzeUtJRJmNK85ElLbcr70KkUiYcewmtmkDQH95NYJ5+Wbc0NkCVeeqEKWui9q1f8chYanKqwiUe6wPKKe1cyvPRESZjoUzEaUtz/PPmo8NVUX84zdIzCZ1tNZbK3VC19HYySOoKLVNX7kVkZrVZlz+DNu1U1m113n72BNl4UxEmY+FMxGlJ02D9/fPmGH8Y9fCKCiUmFBqGM0twGBZwIyr+zuREw1LzIhoAWZO1z51AjlnWyQmRPPJVQVK3daq83jCwHCMXS1ElNm4IYWI0lLenm9BGRkxY72sHL7Hfy4xo9Twfg2PoKL0NHzbHaj92Y/MuOx3/42u+78qMSOaT7VXwUjc2hP9/pSGnaVcjyGizMXfcESUllzHP3DEiSs2S8okdURcbpyqajDj4vFhlIwPS8yIaOGmr9qGSHWtGfNYqtSW7xIotJ1N1RbWMRHnqjMRZS4WzkSUfgwDrg+twlmrroFRXCIxodRwsqoRCdVqJOIRVJRWzk/XvsB/8kP42s5ITIgupcbr/A3zwQKmchMRpSsWzkSUdtQTx6GMWm3aic1XScwmNWhC4P1aq03bFwmjpq9DYkZEl294l3O6NoeEpbZCl4DfOtYZLUEdQY2DwogoM7FwJqK04/3dbxxxYvOVkjJJHa0VdZj25Znxmq7TUAy2TVJ6mdqyHZGqajMue5bHUqUyIYRjwrYO4PgUV52JKDNxOBgRpR3P735rPtZLS6FXVknMRj4DwJHVm8xYTSSwpuOUvISIFqjylyeXeBIAACAASURBVI9d9Fx4TRN8fT0AAP/x91H744cQLy0DAPR/6rMrmB0tRKlbwKcAkfOf0zUHNWwpUOFVuFGEiDILV5yJKK0oXZ1wf3DMjBNXXAmI7L5B6yypwqi/yIzru1vhjcckZkS0eNObtzpi//tHJWVCCyGEQI1t1TluACe515mIMhBXnIkorVzUpn0l9zcfrbNWm4WuY237SYnZECUnUrMa8aJiuMfHAAD57x/G2I2fyPoPyFJZuUegMwLEzm9vfn9Kg1sA6gL+m22wb5ImIkphXHEmorTiaNPO80Ora5jn6sw3kF+KnuIKM24a7EReOCgxI6IkKQqmrtpmht7BAXj6eiUmRJeiCIHAjFXnwRiHhBFRZmHhTERpQ4yOwP3WG2acuGIzoGT3r7GjdRsd8bZOrjZT+pvast0RFxw7LCkTWqgKr4DtWGf0RHToBotnIsoc2X3HSURpxfPcsxC6NSk626dpj+f4caa81oxrR/tQPj0mMSOipRGrDCBaYQ39879/BNA5JT6VuYRApceqnKMGMBxn4UxEmYOFMxGlDa+tTdvweKCtXScxG/mO1W5w7Pvc1nFCYjZES2tqy9XmY/fkBHLaz0rMhhYi4FUcN5Y9ER0GV52JKEOwcCai9BAKwfPyi2aYWL8RcHvk5SNZyO3Fyao1Zlw2NYqasQGJGREtramrrnbE+WzXTnluRaDCtuoc0oGxBAtnIsoMLJyJKC14XjkAEQ6bcba3aX9Qsw6aah2McHXHSXDmMGWSRHEJwvXWh0P+D49BRKMSM6KFCPict5bdEbbYE1FmYOFMRGnBfgyV4XIhsfEKidnIFVNd+KDGalPPD0+jcahTYkZEy2PSNiRMjYRR8vLzErOhhfApAuVu62O8KQ2Y4KozEWUAFs5ElPqiUccxVPFrrgdycyUmJNfJqjWIur1mvLWzGQr3EVIGmt68BYZqnfO76r+flJgNLVT1jFXnHq46E1EGYOFMRCnP89yzUCbGzTj6v2+XmI1cmlDODQU7zxeLYGPfGYkZES0fPTcPwbXWz3vJS7+HOjkhMSNaiDxVoMR2NtVYwsA0V52JKM2xcCailOd78j/Mx4bHg+jt/0diNnKdrFqDqRy/GV/Z0wK3rknMiGh52c90VmJRlP3+N/NcTamiZuZe5yhXnYkovS1J4ZxIJPDYY49h165duOqqq3DzzTfj0UcfRTweX9D7x8fHsWfPHuzcuRNbtmzBnXfeiWeeeeaS7zt58iQ2bdqEF154IdkvgYhSlBgdgefF58w49kd/DKOoWGJG8iQUBYfqN5uxOxHHld2nJWZEtPyCG66A7rG2JpSzXTst5LsECm2rziNxAyGNq85ElL6WpHDes2cPvvvd76KoqAj33HMPKioq8Mgjj+ArX/nKJd8bCoVw7733Yv/+/diyZQvuvvtuTE5O4stf/jL27t075/uGhobwwAMPQNO40kKUybxP/QrC9iFc5M93S8xGruOBtQj6rL3dW7qakRPnlGHKbIbHg+krrCn6RW+9Cs9An8SMaKFqOWGbiDJI0oXzkSNH8MQTT+DWW2/Fvn378NWvfhX79u3DHXfcgd///vc4cODAvO9//PHHcfz4cXzzm9/ED37wA/zt3/4tnnrqKaxduxYPPfQQRkZGLnpPc3Mzdu/ejc5OTpElynT2Nm29uBixT9wiMRt54oqKw/WbzNgbj2JrZ7PEjIhWjr1dWxgGyp/+lcRsaKEKVCDfmu2GobiBMFediShNJV0479u3DwDwxS9+EUKca8kRQuDBBx+EEAJPPjl/S9X+/ftRVlaG3butVSS/34/Pf/7zCIfDePrppx3Xf//738ef/dmfYXh4GNu3b5/5xxFRBlHPtsJ9+KAZR2+/E/B4JGYkz/u16xH25Jjxts6T8GoL2w5DlO5Ca9YikWft7V/16/+Y52pKFUKIi1ade7jXmYjSVNKF86FDh1BcXIx169Y5nq+oqEB9fT0OHjw4xzuBzs5ODAwMYPv27VBtx00AwI4dOwDgovf/67/+K6688kr86le/wsc+9rFk0yeiFOZ98glHnK1t2lHVjaOrN5pxTiyMq7pOScyIaIWpKqa2XG2G/pMfIu/4+xITooUqcgnk2W7xBmMGojpXnYko/biSeXMsFkN/fz+2bNky6+vV1dVoa2vD6OgoSkpKLnr9Qqv16tWrL3qtvLwcXq8X7e3tjud/9rOf4YYbbkgm7TmVl+cvy5+b7fh9pUUxDODXto6VpiYU33YzcL6zBfk+OXmdl7+C//73Ahsd5zZf038aJXluAO5Zrx8XXImmxfP5Zv+5ki16zbXAm6+ace1Tv0TPH35UYka0UOtcCRwdiQAADACDuoJNhed+p5WXrczvUt6LUCbiz/XKSmrFeXz83Lmq+fmz/0e78PzU1NS87y8oKJj1db/ff9F7l6toJqIU8+abwNmzVvyZz1hFcxYJqx4crGwy4/xYGNsG2yRmRCRHvKoaoa3WqnPxfz0BEYlIzIgWalWOCr/LuuXsDsYR1diyTUTpJakV50QiAQDwzLHn8MLz0ejsU18X8v5wOJxMipdlaGj2Ap8W58KnYPy+0mL4/9+/IccWj9x2B3Tbz5JvSs4N84WV5qkV+ve/2bgBMdVaAbz67AcIT4bmfU+EewhpES6sNEciqdux0Hvnp9H03hEAgGt8DJ5f/wrD/+tOyVnRQlR5gJZzt33QDeD0SBj1OSqGjOX9eeO9CGUi/lwnZ7Er9UmtOPt8524g5zqvOfb/t3fn4VFU6f7Av1W9dzrprCRkgbAlYQ2b7LLJwKAIOOogAsrggqPi1cF7XUYZ56q/8TqKoygXZcYV9ToiboiKKAJu7JvsSxKSQEK2TtJJp7eq3x8hlTRZyNJJdZrv53n6SZ9Tp7rfwOlOvXWqznG5AAAmk6nB7QaDwaddQ/ubzeYGtxFREKuqguGzj5Wi+4qRkHr0VDEgdVTojTiYWDt/RJjDjr7nTjexB1FwOz/zBngNtZf2xn34rorRUEvE6ATUnSfsnFOGm/c6E1En0qbE2WKxQBRF2O32BrfXXGbd2KXcVqsVABrd3263w2KxNLiNiIKX/puvIZbalHLV7+eqGI16dnfvD4+m9sKgKzIOQiNzNJkuX94wK4qmzVDK4T9shiE3W8WIqLkEQUCCofawUwJwzsXEmYg6jzYlznq9HvHx8cjJyWlwe05ODiIiIhAeHt7g9uTkZKXdxc6fPw+n04kePXq0JUQi6oTqrt0s6/VwzrpOxWjUUW4w41BC7b3N4RWlSMnPVC8gogCRf+N85bkgy+iyjktTdRZd9AL0daaqOOuUOMM2EXUabV6OatiwYSgoKEBGhu9kNfn5+cjKysLgwYMb3Tc+Ph7x8fHYvXs3JMl3FGXHjh0AgCFDhrQ1RCLqRISiIui/3aiUXb/5LeTwCBUjUsdPvYdAEmvXcBmRcRCizANMItvoK1GVkKSUY9e+C0i8EqMzEAUBiXWu1/bKwKFyr4oRERE1X5sT59mzZwMAXnjhBSX5lWUZy5cvhyzLmDNnTpP7z5w5E3l5eVizZo1SZ7fbsWrVKhiNRsyaNautIRJRJ2J87x0IdeZNuBzXbs6OiMXJ2O5KObq8GL3Pn1ExIqIAIorIv2GeUjRlZ8G6/UcVA6KWiL1o1PlXu5ejzkTUKbRpVm0AGDNmDK6++mps2LABc+bMwciRI7F3717s2rUL06ZNw8SJE5W2K1asAAAsWbJEqbvjjjvw1Vdf4emnn8bOnTuRlJSEjRs3Ijs7G48//niD6z8TUZDyemF685+1xdg4uKZMVTGgjucVRGxLGe5TN/74Llx+C3ERNS7/hrno9tL/QLhwFUbsh2tQOvpKlaOi5qgZdT7tqB5sccvAwXIvhlvbfEhKRNSu2jziDADPPvss7rvvPpSUlOCtt95CYWEh7rvvPjz33HMQ6qy7+vLLL+Pll1/22ddiseDdd9/F9ddfj127duG9995DWFgYli9fjvnz51/8VkQUxPTffA1Ndu3IatUtfwAaWa4uWB1ITEFJiFUpp547ja6lhSpGRBR4nAndYBs7USlHf/kZNGWl6gVELRKrF2CoczbwkN0Lh5ejzkQU2ARZ5k1zNbgWmn9xjTlqKeuNs6DfshkAIGu1KN57GFJsXINtjW+/0ZGhKdpzHecKvQnvjpoBt7Z6PV29x4V5P6+H2d2y98rjOs7UCp1hHee8uQuV5zGff4S0/7hdKZ94ajnybv6DClFRa+Q7JZx01H5XDbRoMCLc/6POPBahYMR+3TatXceZ18UQUUDQnDiuJM0A4Lx2VqNJc7D6sfcQJWkGgBGnD7Y4aSYKZnHvv6k8F9xueI0maKocAICkVf8AhPo3NdRNtilwxOgF5DiBqgu58+EKLwaEamDW8MYUIgpMTJyJKCAY31jtU3bcdpdKkagjN7wLTsQlK+UoewliTh9FHi8KImqQrNOhPH0Ywrf/AAAw5pyB/mwuXPEJKkdGzSEKApKMIk5UVmfOXhnYX+7F6HYYdSYi8ge/3ONMRNQWgr0cxv97Tym7B6bDc8UIFSPqWF5BwNYGJgTj8lNETSsbPtKnXJNEU+cQoxNg1daOMB+ze1Hh4fceEQUmntYjItVZlv4HRHvtfTqevv1gfOdN9QLqYAcTU1BsCVfKKXkZiLcVIE/FmIg6A2d8IhxJ3WHKzgIAhO7fg8LfzoRkMqkcGTWHIAgYEqbB98UeAIAXwL5yD8ZG6JrekYhIBRxxJiJ1yTJ0P26rLZrN8AwZqmJAHatCb8SOHoOUss7jxpiTe1WMiKhzKR01Tnkuul0I27tDxWiopXqaRITXGXU+XiGhnKPORBSAmDgTkap027ZAcz5fKbtHjAJ0wbMEVZ5TavKxqedQnwnB0k4cQHl5JWfGJmome/90eMwhStm6/UeAtzl0GoIgYGiYRilLqB51JiIKNEyciUhVpn+9pjyXBQGu0eOaaB1ccuK6ISc+WSmHldvQO+uoegERdUKyToey4aOUsr6wAKZTx1WMiFoq2SQiUlc76nyiQkKpmycPiSiwMHEmItWI2Weg/3qDUvb06w85KkrFiDqOw2DC3v51JjaSZQw5tJ0TghG1QumIMZDrLEUVvv1HFaOhlhIEAcPqjDrLAHaUetULiIioAUyciUg1pn+9BkGqHVVwj71SxWg6jgxgz4CRcOkNSl2fjCOILilQLyiiTswTEYmK1H5KOeTIr9DaSlSMiFoqySgiVl978uNMlYTcKo46E1HgYOJMRKoQbCUwvvW6UvbGdIG3d4qKEXWcrMReyOuSqJTDym3of2KfihERdX51JwkTZBlhO39WMRpqKUEQMOqiNZx/sXkg8SocIgoQTJyJSBWmf70GscKulN0TJwNi8H8lVZhCsL/vMKUsSBKGH/gJGokjK0RtUdkrBa6oaKVs3fUL4OEkU51JtF5EH3Pt3wGbR8axCn43ElFgCP6jVCIKPBUVMK3+X6UoWa1wD7tCxYA6hgxg98DR8GhrZw1PO3UQEWXF6gVFFCxEEaUjxipFrb0clkMHVAyIWmOYVYs6q1Nhd5kHTomjzkSkPibORNThTO++BbG4Nll0TZgEaLVN7BEcTnVPRUFUnFIOLy1C2qlfVYyIKLiUDb0CUp3l3cK3/6BiNNQaIRoB6aG1E4U5JWBvGScKIyL1MXEmoo7lcsG0coVSlCIi4B45RsWAOkZ5SBgOpg5RyqLXiysO/MRZtIn8SDKHoDx9qFI2ZWXAfIQnpzqbAaEaWGpzZxy2e7k8FRGpjokzEXUo49oPoDmbq5Qdt98FGAxN7NH5SYKAXYNGQ9LUjqr3P74PYfZSFaMiCk6lI8f6lBNe/99GWlKg0goCrrDWfl/KALZzeSoiUhkTZyLqOF4vTCteUIqyOQSO2xerGFDHOJQyGMXhMUo5ujgffTKPqBgRUfByJiTB0S1ZKXf57EPo886qFxC1Sg+T7/JU2VUScrg8FRGpKPhvKiSiDmd8+40G67X790F76qRSdl8xAobPP+2osFRxLiYBx3v2V8pajwvDDvwMoYl9iKhtSsZNgum96u8h0e1G/JuvIvPhv6ocFbVEzfJUn553K3XbbR7Ex+ogCvwGJaKOxxFnIuoYsgz9d9/UFjUauMZPUjGg9mc3mLBr0GifuqEHt8PisDeyBxH5Q0XfAXBF1V7l0fW9N6ApL1MxImqNhpanOmLnJdtEpA4mzkTUITTHj0KTm6OU3cNHQLZaVYyofUmCgI39x8KlNyp1Pc4cR1JelopREV0mRBEl4yYqRa29HHH/95Z68VCrXbw81a4yL8o8nFSRiDoeE2cian+yDP23dUabBQGuSVepGFD7295jEM6Fd1HK1rISpB/ZrWJERJeX8iHD4QmxKOWEN1ZBcLlUjIhaI0QjYGhY7RTbHhnYVuyGzBUJiKiDMXEmonanOXEM2tOnlLInfQjk6Jgm9ujczkR2xZ7k2vuaNR43Ru7bBo3ESwyJOoqs06N01JVK2ZB3FjHr16kYEbVWf4sGMXUmCstzyThSwYnCiKhjMXEmovYlyzB8+UVtURDguuo3KgbUvir0Jmzqd9F9zYe2I7SC91cSdTTbqLHwmsxKOfG1lwCOVHY6oiBgfIQWdZZ2xs5SDy/ZJqIOxcSZiNqV9tCv0GSfUcqeIUMhdY1XMaL2U31f8xg46tzXnJx9Et3OZqoXFNFlTDKHIO/385VyyPEjiNiyScWIqLXCdSKGWXnJNhGph8tREVH7kSTov6oz2iyKcE6drmJA7WtHj4E4GxGrlCPtNqQf3qliRESUu+huxL/zTwhS9aW9iatXoGRi8F710tkcbcEs2VoAoRqg/MIuNZds97NomtyPiMgfOOJMRO1Gu28PNHnnlLJ7xKigvbc5IzoBu5MHKGWt14Npv/4ALe9rJlKVM6k7Cq+epZTDf94Gy8F9KkZErSUIAnqbNai7ijMv2SaijsLEmYjah9cLw8YvlaKs1cI1ZZqKAbUfm8mCTX1972ueeGwHIit5XzNRIMi54z6fcuKrL6oUCbWVWSOgm7H28JWXbBNRR2HiTETtQrdrB8TCQqXsHj0Wcni4ihG1D7eowVcDroRLp1fqBuQcR2pepnpBEZEP+8DBsI2unWE7ZsMnMB/5VcWIqC0SDEK9WbYPc5ZtImpnTJyJyP/cbug3fqUUZb0ersnBd0+hDGBL6hUoCo1Q6rqUFmLciT3qBUVEDTpzz4M+5e4vPqNSJNRWQkOzbNs8KHQxeSai9sPEmYj8TvfLjxBLbUrZdeVEyKGhKkbUPg7F98axrj2VstFVhd/++gM0Mg/eiAJN6ZjxPqPO0Ru/gOXAXhUjorYI14kYWmeWbS+Ab4vccEq8ZJuI2gcTZyLyL7sd+m+/UYqyyQTXxEkqBtQ+8kOjsC1lmFIWZAlTD/2IUGelilERUVOyHnjUp9z9hf+nUiTkDwMtGiTWud/Z7gW+L/bwfmciahdMnInIr8wvLYdotytl18SrAJNZxYj8r1Krx1cDx0ESa0c7Rpw+gKSSfBWjIqJLKRs+CsUTrlLKkVs2IWzXLypGRG0hCAImRGhRdzWqnCoJ+8q5mgER+R8TZyLyGzHjNMwrX1LKUmgYXOPGqxiR/0kQ8FnPK2A3hih1yQU5GJZ1WMWoiKi5sh74s0+Zo86dm1EjYHKUzueAdk+ZF7lVvGWGiPyLiTMR+Y3lL49CcLmUsnPGTMBgUDEi/9ua2A+Z1lilHFZZjilHfvZZV5SIApd90BAUTr1GKYf/vA3Wn7aqGBG1VYxexOhwrU/d5mI37FzfmYj8iIkzEfmF7rtvYPhqg1L2dk+GZ8iwJvboXPKcEnZFJOKXrqlKncbrwYg9W1BS4USeU2rwQUSBJ+v+RyALtae7ur/wNMD7Yju11BARfcy1h7VOCfiuyA0PJwsjIj9h4kxEbedywfLYw0pRFgRUzb4eEIPnK6bMYsWugaN96oYe/AXWclsjexBRoKpM64+Ca65TytbdOxCxZZOKEVFbCYKAMeFaROpqT4gUuGV8m1uhYlREFEyC56iWiFRjWr0K2pMnlLJ7xChISd1UjMi/nBodfh4yAV6tTqnrnXEE3c5lqhYTEbXNmfsfhlzn5F735Rx17uy0ooCronTQ17l3Zm9hFfYWOtQLioiChvbSTYiIGifm58H83DNKWQqzwvXba5rYo3ORAWzqNxp2S5hS16XkPAYe26NeUETUbHHvv9notvL0YQjbuxMAEPrrfvR56F6cePaVDoqM2kOYVsD4SC02FXmUuo3ZFZgarfNZuoqIqKX4DUJEbRLy1BMQK2qXn6r8r0cgh4aqF5Cf7U7uj8yYRKVsqqrEuEM/QeTIFFGnVzR5KiRN7VpG0V99Dk1ZqYoRkT90N2kwNKz2/1VG9f3OxW7OO0FErcfEmYhaTbtrB4wfvKeUPalpcPzhDhUj8q+syK7Y3mOQUhYlL8Yf+hEmt1PFqIjIXzyR0bCNm6SUtfZydHvpWRUjIn8ZHKpB7zqThbllYGOhG5VenvQkotZh4kxEreNwIPT+e3yq7E8/C+h0jezQuZSaLPim/1igzsy76Yd3IrqsWMWoiMjfiidOgdsarpQT3noV5uNHVIyImuOo3dvk41iFhBidgAhD7aFuhRf4ppAzbRNR6zBxJqJWCXnqL9AeP6aUnTNmwT1+onoB+ZFb1ODLAVfCqdMrdcnZJ9Az+6SKURFRe5D1BhROn6WUBa8XvZ74L04UFgREQcCQKBPM2toToIVuGVtKPJD5/0tELcTEmYhaTPf9dzCvXqWUpeholD/zvIoR+Y8MYHPaSBSFRih1XcqKMPjwTvWCIqJ2ZR+QjspefZRy+C8/IPqLj1WMiPxFrxEwLNqEOrkzMh0SdpZ61QuKiDolzqpNRC0ilBQj9L4/+tSVP78CcpcuKkXkXwcSU3EiLlkpm1xV+O3BbaiQOKkMUdASBBTM+B26rfg7hAuf9Z7/73EUT5oKKcSicnDUViE6EWkhGhyye1EzznzQ7kWFV0ZXQ/PHkNIsmks3IqKgxRFnImoRy8NLock7p5Qd826Ba3pwLD+VG94FP/YeopQFScLUX39AqLNSxaiIqCO4usTBNma8UjbknUW3l59TMSLyJ6tW8JksDABOOyQUuXhSlIiah4kzETWbYd2HMH78kVL2dktGxZN/UzEi/7EbTPh6wFjIYu3X4phT+5BoO69iVETUkYonTYOzS5xSTnh9JUynjqsYEflTF72IpIvWcj5eKaHMw/udiejSmDgTUbOIuTmwPLRUKcuiiLJXXoNs6fxrNnsFEV8NuBIOvUmp65OfifTsoypGRUQdTTIakfHwX5Wy6HYj5cG7IbjdKkZF/pRkEBCrr73hWQJwpMLLZaqI6JKYOBPRpblcCL3nToilNqXKseQBeEaOUjGo1slzSvUeX/cainxrtNImrLwE/fb/jPw6bYjo8lAw60aUjhijlMP270bSK8Ex+SEBgiCgl0lERJ3ZwjwycMjuhZPLVBFRE5g4E1HTZBmW/3oA+p9+UKrcAwah4j8fUTEo/8lI7I2MbilKWed2YfSeLdB6OeMq0WVJEHD82VfgqTMpWLdXnkPoXs6sHywEQUBqiIi6c325ZOCw3cs1nomoUUyciahJphUvwPTeO0pZNoegfOVqQK9vYq/OoTAiBnv7X+FTd8X+H2GptKsUEREFgqpuyTj1l/9RyoLXi9Q/LYZYwe+GYKERBPQL0aDuLc+VEnCkUoLENZ6JqAFMnImoUfrPPoblqSeUsiyKKHvtdXjT+qoWk79UGEPwy5AJkMXaIYd+J/aja0GuilERUaA4f/1cFP52plI2ZWWg51N/VjEi8jedKKB/iAa6Oms8l3lkHK+UIDN5JqKLMHEmogZpd+1A2D13+tRVPPk3uKZOVyki//FoNPh52AQ4DUalLuFcFtJOHlQxKiIKKIKAE0+/4DPLdtcP3kbUxi9UDIr8zaipHnmuu0JzkVvGKQeTZyLyxcSZiOoRszJhveUmCE6nUue47U447vijilH5hwxg18AxKA2LVOqsZcUYfvAnCI3vRkSXIU9EJI7//RWfuj6P3Afd+TyVIqL2YNEKSAsRff4G5LtkZFUxeSaiWkyciciHUFgI67wbIRYWKnXOKVNhf/IZFaPyn6O9BiK3a3elbHBWYfRuTgZGRA2zXTkZuQvvUsq6kmKk/ukuwONRMSryt3CdiBSz72FxrlNGrpOJMxFVY+JMRArh/HmE/+4aaI8fU+o8/Qei/LU3AK1Wxcj843R0Ig6npCtlQZIwau8WhFRVqBgVEQW6zP9ahoqUNKUc8dMW9Hya9zsHm2i9iN4m30PjrCoJ57gkIREB6PxHwkTkF2LeOVivvxbaE8eVOm9sHErXfADDurUqRuYfRSFWfNNvtE/d4MM7EF1SoFJERNRZSEYTjr6wGoNvmAaNoxIAkPDWa6jsk4a8m/+gcnTkT7EGER4ZyKyqTZZPOyRoBSDN505oIrrccMSZiCDm5sA6a3r9pHndekgJiSpG5h92gwnr0yfCo9UpdT2zjqFn9kkVoyKizqSy7wAce36VT12vJ/4L1p+3qRQRtZcEo4hEg++sFycqJZxx8JYeossZR5yJLnPimSyE/+5aaM5kKnXe+ASUrvsc3p691QvMT5xaHT5PnwS7MUSpiynKQ/qRXSpGRUSBKu79N5vcXjhlOqI3fQkAED0e9L37Fuz7+FtUJffsgOioo3QzivDIEvJc1fc4ywC+K/JgSrSARCPHnYguR0yciS5DxrffAAAIBedhfvUViDabsk2KiITj1tug+2EbdD907pEUryDiy4HjUWwJV+os9jKM3LsVImdKJaJWKJn4GxjO5yH0wF4AgK7Uhv533ox9a7+GN8yqcnTkL4IgoKepOnkudFf/vfAC2FToxlVRWiSZeNk20eWGp8yILlNiVibML//DN2mOikbl3UsgR0WpGJl/yAA29RuN4gzAXQAAIABJREFU3IhYpc7sdGDcrm9hcLvUC4yIOjdBQP7vbkJVQpJSZT55DGn/cRsEt1vFwMjfBEFAH7OICG3tZdteAJuKPLxsm+gyxMSZ6DKkOXQQ5lUvQ6yonU3aG9MFlXffBzkisok9OwcZwI+9h+JkbO2yUzqPGzP2f48QB2fQJqK2kXV6nJ1/GzyhtSPMkVu+RcrSuwAubRdURKF6jefIOsmzBODbIg8ymTwTXVaYOBNdZoxv/gumN//lMzLiTUiE4+4lkK3BcZnhvqQ07O9Wu3SMKEmYfnArYuwlKkZFRMHEG2bF2fmL4DUYlbou69ehz6P3AxKXLwomoiAgNUREcp2lqiRU3/OcUcnkmehywXuciS4Xsgzz355EyD+e86n2pPaF45Y/AAaDSoH51/HY7vipz1CfuslHfkFSSb5KERFRsHImdkPe7+cj/t3XIVxIluM+XANDThYKr7kOEIR6++TNXdjBUZI/iIKASZEafF/sQYaj+v9aBrC52AMZQE8z73kmCnYccSa6HLhcCF1yV72k2X3FSDgW3RE0SfOJLt2xqa/vWs2jT+5Fan6mOgERUdCrTOuPvN8vgFwnSY74eRuivtmgYlTUHkRBwMRILXrWGXmWAXxf7MFRO0eeiYIdE2eiICfYSmCdcx2M/37fp975m2mo+v1cQBMcZ8kPd+2Jjf3HQBZrv9YGZR/DkDNHVIyKiC4H9oGDkf+7m3zqIrdsQsT336gUEbUXURAwIVKL3mbf5PlHmwe/2DyQuGIDUdDipdpEQUzMzID15hugPXlCqZM1GjivuwHuUWNUjMy/DiSkYFvqcJ+6lLwMjD2xB/UvlCQi8r/yoSMgulzo8vlHSl30NxsAQUDJhCkqRkb+JgoCrozQQoQHxytr72c/ZPei1CNjUqQWepF/fYiCDUeciYKUdtcORFx9lU/SLIVYUPbO/wVV0rynW996SXO/3JO46vAvEMEz/0TUcUpHjUPBb2f61EVv/AJRX38OcCQyqIiCgHERWgwJ9b1qK6dKwufn3Sj38P+bKNgwcSYKQvrPPkb472ZALCxU6rxd42H7/Gu4pkxTMTL/kQFs7zEIP/ce4lOffuYoJh7bwaSZiFRhu3ISiq76rU9d5NbvEPPZR5xtO8gIgoChVi0mRmpRN322eWR8dt6FfCf/v4mCCRNnomAiyzCt+Aest98KoapKqXYPGATbV9/BO2CgisH5T806zbt6DPCpH57xK8ae5OXZRKSu4snTUPDba33qwnf8iNi17/ksBUjBoZdZg6tjdKgzZxiqJGBDgRtH7V7IvNqAKCgwcSYKFm43LA/eD8uTy3yqnVOmovSzLyF1jVcpMP+q0uqxYdAEn3WaAWDUqX0YmXGASTMRBQTblZORP+tGn9m2w/bvRt97boXgrGpiT+qMuhhEzOyiR6Su9v9bQvWkYRuLPKjwMnkm6uwEmafBFAUF5WqHEFRiYkIB8N+1Ixhf+1+Y3n4D2uNHfepdY66Ec9Z1QTNzdn5oFL4eMBblJotP/ZXHdmFQ7vFmvUZeGy+dMxp1AICqKo4aUXBh324flv17ELf2XWWdZwAoHTEGh1e+DU9klIqRXR5CQ40AgPLytp+sSLNc+m+pW5KxpdiDrCrfvzV6ARgdoUUvkwihgfW9iVqCx9htU/Pv11IccSbq5MTcHJhf+YdP0iwLAqpmzobzuuuDImmWARxITMG6YVN8kmZR8uKqwz83O2kmIupo9vShODdvESRt7UIm1h0/YfD1v4HpFL+7go1OFHBVlBZDwzQ+V0C5ZGBLsQffFXvg4OgzUafEEec6eNbGv3g2rP1p9+5G2C1zocnPU+pknQ5V826BZ8AgFSPzH5dGi+/SRuJUbHefenOlHaP2bkVEWXGHxsNROQpW7Nvty3T6JLqu+Sc0TqdS5wkNw5FX3oJt3ET1Agty/hxxbim7R8aJSi8qL7rQySgCo8O16MHRZ2olHmO3DUeciS4nsgzjW68j/NppPkmzFBqKyj8uCZqkOS8sCv++4rf1kuau+dm46scNHZ40ExG1lqNnb+Qs/g9UJSQpddryMgz4ww2Ie/d1FSOj9mLRCkgP1SA91Hf0uUoCNhd7sL7AzZm3iToR7aWbEFFAqaxE6H/eD+OH/+dT7Y2Ng+O2OyEHwT1zFXojfumVjqNde/nUC5KE0af2Ie7kYU4CRkSdjiu2K/Z9/C363TUPYXt2AgAErxd9Hl8K88ljyHjkSch6vcpRkj+JgoDhVg26GUVsLfGgtM76zuddMtYXuNHdKGK4VYNwHceziAIZE2eiTkRz+iTC/rAA2iOHfOo9ffvDcfN8wGRWKbLWuXiiLkkQcbJ7Ko70GQiP1vfg0VRVgRF7f0C0raAjQyQi8it3dAwOvPsZUh5agi6frVXqE956DWF7duLoP1ajqkevJl6BOqMuBhGzu+iwq8yLw3Yv6t4nmVUl4UyVhJQQEUPDtDBreGqYKBAxcSbqDGQZhk/XwbL0PyCWl9VWiyIqHnkcsjUcEDv3meq86K7Y33c47BZrvW3xeWcw5NB2GF3OBvYkIupcZIMRx154DZW9U5G8/GmlPvTgXgyZORGn/vvvOH/dTSpGSO1BKwoYFa5FWoiI3WVeZDpqTx7LAI5VSDhZ6UKKWcSAUC3CtEygiQIJE2eiACeeyYLl4aUwbNroUy9FR6Ns1etwj58I49tvqBRd28gAzkfF4XjPfjgfXX+d6VC7DemHdyG2KK/+zkREnZkgIPveB1HZKwUpDy+B9sJJUW2FHalL/4jwbZtx6r+fg9fSuklsKHActXvr1SUYRIRqBGQ5vCirs9krA0cqJBypcCHZJGJQqAYx+s59YpwoWDBxJgpUbjdM//syQp5/BoLD4btp+AiU/fMtSPEJKgXXNl5BwMku3bEzKQ2lYZH1tuvcLvQ9sR+9zhyHyIn/iSiIFU2fiT0DByPt/tuV+54BIPaTfyNs706cePofKB0zXsUIqb2EaQUMsGhQ7JGR5ZDguGiesEyHhEyHhDi9gIGhGiQZOQs3kZqYOBMFIN3PP8Ly0J+gPXrEp14WRTjuuhcVjy4DOuEEMk6NDofje2F/UhoqjA3cjy3LSM45if7H9/GybCK6bDgTu2H//21A95f+B0mvPA/hwglDU1YGBs2fhYJrrsPpR5+Eq2vnPFlKjRMEAVE6AZFaAQVuGblVUr3lq/JcMvKKPAjVAGkWDfqYNTDxPmiiDsfEmShQeDzQf7ke5lWvQLdze73N7iFDYX/uRXgGpqsQXOtJgoDsiDgcj0vG6ZgkeDQNfO3IEhLyspF6+hCXmCKiy5NWi6w//Rm2MROQ+qfFMOSdVTbFfPExIjdvxJl7H0Tuors583YQEgQBXfQCYnQCbB4ZuU7ZZwZuACj3AjtLvdhd6kUPk4g0iwaxeoGj0EQdhIkzkcqEslIY17wN079ehSb7TL3tUpgVFX/+C6pu+QOg0agQYcvJAApCI3A8tgeOx3aHw2BqsJ3G40Fyzkn0zjoKS6W9Y4MkIgpApaPGYc8X29Dz/z2G2I/eV+o1lRXo8exfEfvhu8h86AkUTZne6SeFpPoEQUCETkCEDojRCzho9+J0peQzC7cE4JRDwimHhAitgFSLBr1MIowchSZqV4Is8wbCGgUF5WqHEFRiYqonNOG/a31CWSn0mzZC/+UXMHzzNYTKigbbuQcPhXPmdZDDwjo4wpaTBAF5YdHIio5HRnQiSkLqz45dw+R0YFDOccRkHIPB7erAKNvOaNQBAKqq3CpHQuRf7NuBx5iVgYgfNsNy+GC9bRWp/XDmnqUonD6r05xUVUNoqBEAUF5epXIkreeUZOQ5JeS7ZLgbOWoXAETqqketI7QC+oZybCyY8Ri7bWr+/VqKiXMd7Hz+xQ+1LzH7DPSbNsLw5XroftwGwd3wwams18OTPgSuKycE/ORfDp0BZ6K6IjMqAdmRXeHUNX75oCBLSCrOQ0peJnoVnIFWkuqt49wZMLmgYMW+HZjyfr8AXd99Hd2XPw1dWWm97ZU9+yD77j+h4NrrIet0KkQY2IIhca4hyTKK3TLyXPUv465LJwBpIRr0CRERoeNVCcGIx9htw8TZD9j5/CtYP9TNXvqpqgqaUyehPX4UYn4etCdPNNlcio6GY+HtcCy8HYavvvBDpP4lA7CZw5AXFoU8azTyw6JRZAkHLnFvVUxZMVLyM9AnPwshLt8DFybORIGDfTuwaezliNr0JcJ2b4cg1f/udFvDUTZ8NE4+9TxcsV1ViDAwBVPiXJfDKyPPJeG8S0YTOTQidQJ6mET0NGu4LnQQCdZj7I7CxNkP2Pn8K1g/1E0lzoKtBNpfD0J7cD80GacbPLipS9Zq4U1Jg3tQOjzpQ4AAGi2o0upwPiwaeWFRyL+QKDc1olxXWHkJuubnoNu5TITZ64+QdGZMLihYsW93DtqSYkRs+w5hu7dD9HjqbZc1GhRdNR3n5v0BtrETL/v7oIM1ca4hyTJK3DLyXTJKmsqgAUTrBPQ0i+hh0sDCJLpTC9Zj7I7CxNkP2Pn8K1g/1BcnzkLBeegO7of24IEGJ/e6mGwyw9OvPzwDBsKTkgYYDH6NrzWjuDIElIVa4YmOQV5YNPKt0U3eo3wxjdeDmKI8dD2fi9iCswipavie7WDA5IKCFft256IpK0XED9/DuuMniI3MFeFI6o6C2b/H+Zk3wNErpYMjDAzBnjjX5ZJkFLhknHfVX9LqYlE6AYlGEUlGETF6ASJn5u5UgvUYu6MwcfYDdj7/CtYPtfGt1yGezYX24IHqkeX8vCbby4IAKbEbPKmp8PZJhTe5R7tO5NKcxNmpN6AoPBrF1mgUh0ejJDwaHm3zR7s1Xi+6lBchtqwIicV50ObnQSN52xJ2p8HkgoIV+3bnpKmwI2znz7Du/Bk6W0mj7coHpKNg5g0omPE7uOLiOzBCdV1OiXMNWZYRYxBxulLCaYcXFZf482wQgQSDiESjiASjCDNn5w54wXqM3VGYOPsBO59/BdWHWpKg3bkDhi8+g/GD9yCWNL3WsBQSAk//AfCm9oOnTx/AHNJBgdZPnCVBQGloBIrDo1EUHoPi8GhUhLTsCyPMYUdsaSHiygoRW1qIaLsNGrn2fTrjvcqtxeSCghX7dicnSTCfOArj2RxEfvd1o7cKyYKAsqEjUDT1GhRNuRpVPXp1cKAd63JMnOuSZRnlXqDQJaHQ3fis3HVZNECsQUQXvYhYffXyWByRDixBdYytAibOfsDO51+d/kPtdkP34zYYvvgc+i/XQ3M+v8nmkjUcnoGD4BmY3u6jyk05LRhRYo1CcfiF0WRrFLya5i9LofV6EFtWVCdRLoLZ3fQBBxNnos6PfTt4aG0lCNu9HaH7d0NfVNhk24o+aSiaMh3Fk6ehPH0YoA2uZYwu98S5LlmWUeYFit0SStwyHM38060TgGi9gAidiEhd9XJXEToBOpHJtFo6/TG2ypg4+wE7n391xg+1eDYXuq3fQ79lM/SbNkIstTXZXorpAvfAQfAMGAQpqdslZ5j2N5dGi/NhUci/8DgfFoUKg7lFr2GxlyHKVoBIWyEibYUIs9sg8muhUUwuKFixbwchWYYhNxuh+3cj9MBeaO1N/z32WEJROmocbGMnomTshOr7ojv5SCMT58ZVeasnFCtxVy9v1dJT4KEaIEInIkwrIEwrIPTCT4sGHKFuZ53xGDuQMHH2A3Y+/wr4D7UsQzx3Ftq9e6Df9j10W7+/5JJRAOBNSKweWR6QDik2tsMOKryCgOKQcCVJzg+Lqp7AqwXvr3W7EFlanSBHlRQiorQQhkYmlaGGMbmgYMW+HeQkCebTJ2D5dT9Cjh6Ctrzskrs4u8ShfMgVKBsyHOWDh8M+cDAkU8tOzqqNiXPzeGUZ5R4ZBo2AfGf1MlfNuay7IQKAUK2AUA3qJdWhWgFaJtVtFvDH2AFO1cTZ4/FgzZo1+Pe//42cnBzExMTgd7/7He68807omrG8js1mw0svvYTvv/8eRUVF6NWrF26//XZcffXV9do6HA68+uqr+OKLL5Cfn4/ExETMmzcPN998M4Q2fhDZ+fwroD7UTic0mRnQHj1cPanXgX3Q/noAYmHTl7AB1feDea4YCec1M+G8egb0Wza3e7gujRbFIVYUWcJRFBKOwtAIFIRGwtOCS64hywizlyLywmhylK0QofZS8M9V2zC5oGDFvn0ZkSQYcrNhOfIrjLnZMJ881qzdZI0GFWn9Ye87EJUpfVGR2g8VqX3hjum4k8gtxcS5ZdIs1beZSbIMm0fGeaeMfJeEYrcMm7vlo9IN0QuAUQSMGgEmsXqJrJoEW8/Lv5sloI6xOyFVE+dly5bhgw8+wLBhwzB06FDs2bMHu3fvxrRp0/DSSy81uW9lZSXmz5+Pw4cPY/r06ejatSs2btyI7OxsPP7445g/f77S1uv14o9//CO2bNmCCRMmICUlBVu3bsWxY8ewaNEiPPTQQ236Pdj5/KvDP9QOBzTZZ2B8YzXEoiKIhQUQC85DLCiAUFIMoQVdXTaZ4B45Gs6rr4Vr+jWQYuOUbU2t49wSMoAqnQE2UyhKzaGwmUOVZLnM1PIPtNnpQGxZIczFhYgsLUJEaRF0Hh4A+xuTCwpW7NuXL21xIcwnj8N86jjMp05A46hs0f7uiEhU9uyDqu49UNWtBxzduqOqW/Vzd1S0qkk1E2f/kWQZVRJQ4ZVR6ZVR6QUqJRlOqfqYxh+MIpTR6TBN7Uh1mFaAUUSbB8mCBRPntlEtcd6zZw/mzp2LadOm4cUXX4QgCJBlGQ8//DA++eQTrFq1CpMmTWp0/1WrVuGFF17AsmXLMG/ePACA3W7HTTfdhJycHHz77beIiooCAHz++ed48MEHfZJkt9uN22+/Hdu3b8enn36K1NTUVv8u7Hz+5fcPtSRVJ8KZmdBkZUCTlak8xKxMaPLOtfqlZVGEZ8hQuMZPhHv8JLiHj2h0feWWJM4ujRblxhCUG0NgN5hhN5pRbgy5kCyHwanTtyperceNLuXF1ZN4lRWhS1kRLM5KCLi8JupSA5MLClbs2wSgejT6XC7Mp47DmJUJY3YmtBX2Vr+cJ8SCqqSaRDq5+hGfCGfXBLi6JsBjDW/XxJqJc/uT5erkuUqqTqxrfjq81T/9dVSiFQCL5sJ91BcuBQ/VCgjRCDBrBJguo8SaiXPbqJY4L126FOvXr8fnn3+OlJQUpT4/Px8TJkzA5MmTsXLlykb3Hz9+PLxeL7Zu3QpNnVmI169fj6VLl+KRRx7BwoULAQA33XQTDh48iJ9++glWq1Vpu3fvXtx000245ZZb8Oc//7nVvws7n39d8kMtSYDDAaGiAqKtBEJJCcSSYgglxRCLiyHm50E8dxaac2chnjsLMe8cBHfbD+hkQYAUHQMpIRFSQiIcC2+DZ/AQyNbwhtvLMmQAbhlwSYD46cdwaXVwaXRwaXVw6vSo1Bnh0F946Ayo1BtRqTfB1crEuC5R8iKyorQ6US4tQmxZISIqyxqdwIuJc/tickHBin2bGiTL0NpKYMzOhDE7C8bcbOjzz0FT5Z9E1GsywxkXD1dcPJxd4+HsmlBbjouHOzIK7sgoyAZjq16fibO6ZLn6XumahLomma5Jrj1+nGlJAGDWQEmkjSJgFAUY6v7UCNALgFYUoBOqk/HOOJEZE+e2aW3i3OY1B3bt2oWIiAifpBkAYmNjkZycjJ07dza675kzZ5Cfn49p06b5JM0AMHLkSADAzp07sXDhQrhcLhw8eBBpaWk+STMADBo0CCaTqcn3CgoVFdDkZAOy7PNwSxKqPF5AliHLAGQZgiRDliRcqKht7/VC8HgAtxtwu6ufezwQPO7qpPRCuWY7PG7AfaGupo27znOvB0JN2eWufh2PG6LbjRJv9etqnS6I7gv1Tie0VQ5oHJXQtfBSsJbyGAywJfZASbcesCX1REm3nrB164HiXmlwm0Ig1L2wqECCcL4IAOAVRUiCCK+oufBT9D0bPvKa9glYlmF2VMBaboO1vARh5TZY7TZYKnyTZDeA8+0TARERkS9BgCciEvaISNgHDa2uk2Voy0qhzzsH/flzMOSfg66oELriokvO3H0xjaMS5oyTMGecbLKd1xwCd0Qk3BGR8IaGwWsOqX5YLPCaLZD0ekCjgSyIgChC1mgAQYDepIes0SDMLUEWNYAo+r7wxSehL1W+8G8CQah+nwvPZbH6fSEItTHUtBEFADXPxepbx5SHdKEMQJJ8tgmyVPtcqvO87v41+6D66jloNJBrfn9RhCxqLjwX6jxvpF64sF/Nayivp7nwvHo/5fdH9WAEAHgtoXDFxTfwT1WdqOpFIKyBWVY8NaPTdUerLyTXrhYm1TKACm/1peQtuXhcK9Q+REGACEAUAPHCf3XdckPbld/14t/9ooLQ2DY0sK3OCzfU1uS0QxAEVDk8PnEKPrHVxupbX7etoNQ1FF9jv59Q96fgW6cRqq8GCMbR/zYlzi6XC3l5eUhPT29we0JCAjIyMlBcXIzIyMh628+cOQMA6NatW71tMTExMBgMyMzMBADk5ubC4/E02Faj0SAuLk5pG4z0325E2KIFEBwOtUMJKPboLrAldIctIbn6Z2L3Cz+TYY+Orf8HUm2yBFOVAyEOOywVZbBUlMNSUYbQynKEVJZDI3G0mIiIApwgwGMNh8cajsrUvr6bnE7oSoqgKy6Crrjwws8LD1sxBK+3VW+pqayAprICxtxsf/wG1A7Oz7wBx154rWWrfYgCLCJgaSCp9sp1k2nAWSe5dvrxEnCPXHfk+1IJd4AsRmRv3eeoo0TpBFwTowu6tb7blDjbbNVr3IaGNjzcXVNfXl7eYOJcs39YWFiD+1ssFpSXlzf7vTIyMuDxeKDVtu7Xau2wfYf4YA1wmSXNHp0e5bHxsMfEoSw2HvaYriiP7VqdJCd2R2l8EtymELXDVBg8LoS4nTB7nMrPMFcl3KXlCHFWIqSqEiaXo/E1kvUaAJqGt1HAqbmslSjYsG9Tmxh1gNUCb3J3eAH4XCAtSdDYbNCUlkBjs0FbaoPGVgJNqa263lYCTXlZiybzpMDR5bO1yP+f5fBGx7T7e1XfVy2jynNhhNorwemtfu68UHZLMtwcj1BFkVuGw2hEvLXttywGkjYlzh6PBwCg1zf8j1JT73Q6W72/40Ky2JL3am3iHNA+/ljtCDqcFkDEhQcRERERBbaBagdA1I7adB2r0Vg94YK7kQmbXC4XAMBkMjW43XBh1uKadg3tbzabfdo29V6CIDT6XkRERERERESt0abE2WKxQBRF2O0NL1NQc5l1Y5dX10zy1dj+drsdFoulWW3Ly8thNpshBto9rURERERERNSptSnL1Ov1iI+PR05OToPbc3JyEBERgfDwhpf5SU5OVtpd7Pz583A6nejRoweA6onGdDpdg229Xi/y8vKUtkRERERERET+0ubh2WHDhqGgoAAZGRk+9fn5+cjKysLgwYMb3Tc+Ph7x8fHYvXs3pItmE96xYwcAYMiQIQAArVaL9PR0HD58uN6o84EDB+BwOJS2RERERERERP7S5sR59uzZAIAXXnhBSX5lWcby5cshyzLmzJnT5P4zZ85EXl4e1qxZo9TZ7XasWrUKRqMRs2bN8nkvl8uFFStWKHVutxsvvvgiAODGG29s669DRERERERE5EOQ5bbP+f/AAw9gw4YNGDRoEEaOHIm9e/di165dmDZtGl588UVlAeyahHfJkiXKvna7Hddffz0yMzMxdepUJCUlYePGjcjOzsbjjz+O+fPnK229Xi/mzZuHvXv3YsyYMejfvz+2bduGo0ePYtGiRXjooYfa+qsQERERERER+fBL4ux2u/Haa6/h448/Rn5+PuLj4zFz5kzccccdPstHpaamAgCOHTvms39hYSGWL1+OzZs3w+FwoGfPnrjttttwzTXX1Hsvu92OFStW4Msvv4TNZkO3bt0wd+5czJ07lxODERERERERkd/5JXEmIiIiIiIiClYcoiUiIiIiIiJqAhNnIiIiIiIioiYwcSYiIiIiIiJqAhNnIiIiIiIioiYwcSYiIiIiIiJqAhNnarH8/HwMGzYMb7755iXbrlmzBqmpqVi3bl2D27///nvMmTMHQ4YMwejRo/Hoo4+iqKjIzxETXdql+vXWrVuxYMECDB06FCNHjsRtt92GAwcONNiW/ZoCRVP92uFw4LnnnsPkyZMxYMAAjBs3DsuWLUNxcXGDr8V+TWorKCjAsmXLMGHCBAwYMABjx47Fgw8+iOzs7HptP/nkE8yePRuDBw/G+PHj8be//Q0VFRUNvi77Nqmpuf3abrfj2WefxW9+8xsMGDAAI0eOxN13340jR440+Lrs1/7HxJlapKKiAkuWLIHdbr9k29zcXDz//PONbl+/fj0WL16MoqIizJ07F6NGjcLHH3+Mm266CWVlZf4Mm6hJl+rX//73v3HHHXcgMzMT119/PSZPnoydO3fi5ptvrpc8s19ToGiqX0uShDvuuAOrV69GREQEFixYgNTUVHzwwQeYO3cuysvLfdqzX5PaCgoKcOONN+KDDz5Ar169sGDBAgwcOBDr16/HDTfcgMzMTKXtq6++ioceegiSJGH+/PlIS0vDm2++idtuuw0ul8vnddm3SU3N7deVlZWYN28e/vWvfyEqKgoLFizAmDFjsGXLFsyZMwe7d+/2eV3263YiEzVTTk6OfN1118kpKSlySkqK/MYbbzTZftGiRUrbjz76yGeb3W6XR4wYIV911VVyeXm5Uv/hhx/KKSkp8jPPPNMevwJRPZfq17m5ufKgQYPk6dOny0VFRUr9vn375LS0NHnBggVKHfs1BYpL9euvvvpKTklJke+55x7Z6/Uq9c8//7yckpIir1ixQqljv6ZA8Pjjj8spKSny66+/7lP/6aefyikpKfLixYtlWa7+zu7Xr588Z84c2eVujKBNAAAJ0klEQVRyKe3+8Y9/yCkpKfI777yj1LFvk9qa269fffVVOSUlRX7yySd92m3fvl3u27evPGPGDKWO/br9cMSZmuXNN9/Etddei6NHj2LUqFGXbP/RRx/hhx9+wPjx4xvc/sUXX8Bms2HhwoWwWCxK/Q033IAePXpg3bp18Hq9foufqCHN6ddr165FVVUVHnvsMURGRir16enpuP3229G3b1+ljv2aAkFz+vXBgwcBANdddx1EsfZQ4Pe//z0AYN++fUod+zUFgk2bNiEyMhK33nqrT/3MmTPRrVs3/PDDD5AkCR988AE8Hg8WL14MnU6ntLvrrrtgsVjw4YcfKnXs26S25vbrjRs3QhAE3H///T7tRowYgREjRuD48ePIz88HwH7dnpg4U7O8/fbbSEhIwJo1azBr1qwm254/fx7PPPMMrrvuOowdO7bBNjt37gQAjBw5st62ESNGwGaz4cSJE20PnKgJzenXW7duhdVqbTABWbp0KR555BGlzH5NgaA5/To8PBwAcPbsWZ/6mgOvuieJ2K9JbV6vF4sXL8a9997rc6Knhl6vh9vthtvtVvrrFVdc4dPGYDBg8ODBOHr0qHIrAvs2qakl/XrOnDl44IEHfBLhuu0AKPfws1+3HybO1Cx//etf8cknn2Do0KHNaqvT6fDwww832qZmwoOkpKR62xITEwEAGRkZrYyWqHku1a9lWcapU6fQs2dPFBQU4KGHHsKoUaMwePBg3HbbbfUm5GC/pkDQnO/rGTNmIDQ0FK+88gq2bNmCyspK/Prrr/jLX/4CnU6HefPmKW3Zr0ltGo0Gt956q0+/rHHq1CmcPn0a3bp1g8FgwJkzZxAdHd1ggpGQkACgtr+yb5OaWtKvb7zxRixevLheu+LiYuzatQtms1nps+zX7UerdgDUOVx55ZXNardhwwZs2rQJL7zwgjKi0ZCSkhLo9XoYjcZ622r+2DVnAjKitrhUvy4vL0dlZSWcTiduvPFGmEwmzJgxAwUFBfjmm29w88034+2338bAgQMBsF9TYGjO93VcXBzWrFmDpUuX4s4771TqrVYr3nzzTaSnpyt17NcUqCRJwpNPPglJkpTbDGw2m5IcXCw0NBRAbX9l36ZA1FC/bszf//53VFRUYO7cucrIM/t1++GIM/lNSUkJnnzySUyaNAlXX311k209Ho/yAb9YTb3T6fR7jEQt4XA4AACHDx9Gz5498emnn+Kxxx7Diy++iBUrVqCyshLLli1T2rNfU2dRWVmJFStW4OTJkxg5ciQWLVqESZMmoaysDMuWLfO5hJv9mgKRLMtYtmwZfv75ZwwYMEC5R7Ql/ZV9mwJNY/26IStXrsS6deuQkJCABx54QKlnv24/HHEmv3nqqafgdDrxxBNPXLKt0WiE2+1ucFvNUhEmk8mf4RG1mCAIyvOHH37Y5+ztVVddhREjRmDHjh3IzMxEcnIy+zV1Gk8//TQ2bdqEBx98EHfccYdSv3HjRixZsgT33Xcf1q5dC4Df1xR4PB4PHn/8caxbtw5JSUlYuXKlkhC0pL+yb1MgaapfX+zFF1/EypUrER4ejldffRVWq1XZxn7dfjjiTH6xefNmrF+/HkuXLkVcXNwl24eFhcHpdNZbTxGovXyk5pIqIrXU9EGdToc+ffrU214zo/aZM2cAsF9T5+D1evHZZ58hISEBt99+u8+2qVOnYvz48Th48CBOnjwJgP2aAovD4cDdd9+NdevWITk5GW+//TZiY2OV7WFhYfXWIa9RU1/TX9m3KVBcql/X8Hq9+POf/4yVK1ciKioKb731Vr3jE/br9sPEmfzi66+/BgD893//N1JTU5XH3/72NwDAI488gtTUVGzfvh0AkJycDADIycmp91o1dT169OiAyIkaZzKZ0KVLF3i9XkiSVG+7x+NR2gHs19Q5FBUVweVyoUePHj5XVdTo3bs3gNoZt9mvKVCUlpbi1ltvxZYtW9CvXz+89957iI+P92mTnJyMoqIiVFVV1ds/NzcXoiiie/fuSluAfZvU1Zx+DVSPFt9zzz1Yu3YtEhIS8N577yEtLa1eO/br9sPEmfxiypQpuPfee+s9xo0bB6D6stZ7771XmdFy2LBhAGqnzK9r+/btCA0NRa9evTruFyBqxPDhwyFJUoN99dChQ9BqtUpfZb+mzsBqtUKn0yEzM7PB7VlZWQCAmJgYAOzXFBicTicWL16M/fv3Y8SIEXjnnXcQFRVVr92wYcMgSRJ27dpVb/99+/ahd+/eygRJ7Nuktub2a1mWsXTpUmzevBl9+vTB+++/ryTIF2O/bj9MnMkvpkyZgiVLltR71MzuWrO9ZqbLKVOmICQkBP/85z9hs9mU11m7di0yMzNx4403NrimHVFHq5nR8u9//7vPLJQbNmzAvn37MGnSJGXNW/Zr6gwMBgMmT56MnJwcvPPOOz7bfvzxR2zevBm9evVSRjLYrykQLF++HHv37sWQIUOwevXqBpebAoBrr70WGo0GL7/8ss+lqqtWrYLdbsecOXOUOvZtUltz+/U777yDjRs3onv37o1exl2D/br9cHIwUkV4eDj+8z//E0888QRmz56N6dOnIz8/H19++SWSk5MbXKuOSA2jR4/GggUL8M477+Daa6/F1KlTkZeXh40bNyI6OhqPPPKI0pb9mjqLRx99FAcOHMBTTz2F7777Dv369cOZM2ewadMmmEwmPPPMM8pl3OzXpLaCggK8++67AICePXti9erVDba788470bNnTyxatAirV6/G7NmzMWnSJJw8eRLff/89hg4d6rO8D/s2qam5/XrhwoVYuXIlACA1NVXZ52I33XQTYmJi2K/bERNnUs3cuXNhtVrxz3/+E++++y6sVitmz56NBx54oMk1oIk62mOPPYZ+/fphzZo1eP/99xESEoIZM2bg/vvvV24/qMF+TZ1BXFwc1q5di1deeQWbN2/Gjh07YLVacfXVV+Pee++td/8b+zWpaf/+/coswR999FGj7W699VYYDAYsXboUXbt2xXvvvYe3334bMTExWLhwIe699956sxSzb5Namtuvp0yZgpKSEgDVKx9s3Lix0XY1t9iwX7cPQZZlWe0giIiIiIiIiAIVL3AnIiIiIiIiagITZyIiIiIiIqImMHEmIiIiIiIiagITZyIiIiIiIqImMHEmIiIiIiIiagITZyIiIiIiIqImMHEmIiIiIiIiagITZyIiIiIiIqImMHEmIiIiIiIiagITZyIiIiIiIqImMHEmIiIiIiIiagITZyIiIiIiIqImMHEmIiIiIiIiagITZyIiIiIiIqImMHEmIiIiIiIiagITZyIiIiIiIqImMHEmIiIiIiIiasL/B7tVuSm8dADmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 329,
       "width": 487
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define the parameters\n",
    "nobs = 10**4\n",
    "\n",
    "# generate the random variables \n",
    "np.random.seed(42)\n",
    "males = np.random.normal(180, 10, nobs)\n",
    "females = np.random.normal(170, 8, nobs)\n",
    "\n",
    "# plot the variables\n",
    "sns.distplot(males, color=\"skyblue\", label=\"males\")\n",
    "sns.distplot(females, color=\"red\", label=\"females\")\n",
    "plt.legend()\n",
    "\n",
    "cles = pg.compute_effsize(males, females, eftype='CLES')\n",
    "print(f\"CLES: {cles:.6f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
